Author: headspin

Are you gearing up for web application testing and torn between Cypress and Selenium? Whether you’re a seasoned software tester or just diving into testing, your chosen framework can significantly streamline your testing process. The domain of Cypress testing is marked by its advanced end-to-end testing capabilities and modern architecture, while Selenium has long been the favored framework among web developers. 

Delving into the key differences between these two frameworks, this blog post aims to guide you in making an informed decision based on your specific testing requirements.

Why Compare Cypress and Selenium?

In test automation, selecting the right tool can significantly influence the effectiveness of your testing efforts. Cypress and Selenium are two of the most popular frameworks, each with unique strengths and capabilities.

Cypress is known for its ease of setup, faster execution, and modern architecture, making it a preferred choice for developers and testers who work in JavaScript-heavy environments. It offers a developer-friendly environment with rich features for front-end testing, boasting real-time reloading, debugging, and an intuitive API.

On the other hand, Selenium has been a staple in the test automation landscape for over a decade. Its flexibility and robustness support various programming languages, browsers, and platforms. Selenium is particularly valuable for projects that require cross-browser testing and compatibility across different operating systems.

By comparing Cypress and Selenium, teams can better understand which tool will best support their testing strategies, considering factors like the complexity of the application, the technical stack, and the specific testing requirements.

Selenium vs Cypress: When to Use for Test Automation

Choosing between Selenium and Cypress for test automation depends on several factors, including the nature of your project, your team’s expertise, and your specific testing needs. Here are some guidelines to help you decide when to use each tool:

When to Use Selenium:

1. Cross-Browser Testing: If your application needs to be tested across various browsers (Chrome, Firefox, Safari, Edge, etc.) and operating systems, Selenium’s broad compatibility is invaluable.
2. Language Flexibility: Selenium supports multiple programming languages (Java, Python, C#, Ruby, etc.), making it a suitable choice if your team is proficient in these languages.
3. Complex Scenarios: For complex test scenarios that require interaction with multiple windows, tabs, or browser actions, Selenium’s robust feature set is well-suited.
4. Legacy Systems: If you’re working with older systems or need to integrate with legacy testing frameworks, Selenium’s long-standing presence in the industry provides more mature and diverse integration options.

When to Use Cypress:

1. JavaScript Applications: Cypress is optimized for testing modern JavaScript frameworks like React, Vue, and Angular, providing an excellent developer experience for these environments.
2. Speed and Efficiency: Cypress offers faster test execution and real-time reloading, significantly speeding up the development and testing cycle.
3. Simple Setup: Cypress requires minimal configuration with its all-in-one framework, making it easier to set up and start writing tests quickly.
4. Debugging and Error Handling: Cypress provides superior debugging capabilities with detailed error messages and stack traces, helping developers quickly identify and fix issues.

By understanding the strengths of Selenium and Cypress, teams can select the right tool to enhance their test automation strategy, ensuring thorough and efficient testing that meets their project’s demands.

Factors to Consider in Automation Tool Selection

Choosing the right automation testing tool involves evaluating key factors like language support, usability, community support, test management, integration capabilities, and reporting. A tool encompassing these aspects can empower you to create reliable and efficient automated tests. Furthermore, comparing Cypress and Selenium offers valuable insights into their unique features and limitations.

• Language Support: Language support is crucial in selecting an automation testing tool. Different tools cater to various programming languages, so aligning with your development team’s language preferences is essential. Cypress is an excellent choice if your team is proficient in JavaScript, whereas Selenium might be a better fit for those inclined toward Java. Some tools support multiple languages, accommodating diverse language usage within a team. Opting for a tool that aligns with your team’s comfort ensures a smooth and efficient testing process.
• Ease of Use: The ease of use is a significant consideration when opting for an automation testing tool. A tool should present a user-friendly interface, enabling test engineers and non-technical team members to create and manage tests effortlessly. A steep learning curve can impede the testing process and reduce productivity. Therefore, a testing tool with clear documentation, a simple setup, and an intuitive testing process is vital. An easy-to-use tool encourages collaboration from team members with varying technical skills, fostering an efficient testing process.
• Online Community and Support: Online community and support are vital when selecting an automation testing tool. The presence of an active online community comprising forums or knowledge bases provides users with access to valuable resources and insights from experienced tool users. This proves immensely beneficial for troubleshooting issues or addressing queries not covered in the tool’s documentation. A robust online community also indicates strong tool support and a thriving user base. Quality community and support save users time and ensure optimal utilization of the tool’s capabilities.

Cypress: A Brief Overview

Cypress is a powerful front-end testing tool primarily based on JavaScript. It is developer-friendly and utilizes a unique DOM manipulation approach, operating directly within the browser. One standout feature is its intuitive test runner, executing commands seamlessly during testing.

Advantages:

1. Screenshots During Execution: Cypress captures screenshots during test execution, offering precise visuals of each step for effective debugging and analysis.
2. Eliminated Wait Commands: Unlike Selenium, Cypress eliminates the need for explicit or implicit wait commands in test scripts, streamlining the testing process.
3. Browser Support: Initially supporting Chrome, Cypress has expanded its browser support to include Firefox and Edge, broadening its compatibility.
4. Real-time Execution and Visual Feedback: Developers execute Commands in Cypress in real-time, providing immediate visual feedback and enhancing the testing experience.

Limitations:

1. Single Browser Usage: Cypress doesn’t support driving two browsers simultaneously, limiting its multi-browser testing capabilities.
2. Browser Compatibility: Currently, Cypress lacks support for browsers such as Safari and Internet Explorer.
3. Multi-tab Functionality: It does not support testing in a multi-tab environment.
4. JavaScript Dependency: Cypress exclusively supports JavaScript for executing test cases.
5. iFrame Limitations: Cypress has limited support for testing within iFrames.

Selenium: An Overview

Selenium stands as an open-source automated testing framework crafted to validate web applications on diverse browsers and platforms. It offers the flexibility to utilize multiple programming languages such as Java, C#, and Python to execute Selenium test scripts. The process of testing with Selenium is commonly known as Selenium testing.

Components of Selenium:

1. WebDriver: This component is the framework for cross-browser tests, ensuring compatibility across browsers.
2. Framework: Selenium offers a framework that facilitates the distribution of test tasks across various environments, enabling simultaneous execution of multiple tests.
3. IDE (Integrated Development Environment): Selenium IDE allows the recording and playback of test cases. It’s an extension that aids in creating reproducible experiments.

Advantages:

1. Cross-Platform Compatibility: Selenium seamlessly operates across various operating systems like Windows, Linux, Unix, and Mac.
2. Language Flexibility: QA teams can choose their preferred programming language, Java, Ruby, or Python, among others, for creating test scripts.
3. Concise APIs: Selenium provides clear and concise APIs, simplifying the automation process.
4. Browser Compatibility: It is compatible with popular browsers like Safari, Chrome, and Firefox, enhancing its applicability.

Limitations:

1. Lack of Automatic Test Result Generation: Selenium needs built-in commands for generating automatic test results, necessitating additional effort for result analysis.
2. Limited Image Testing Support: More support for testing images within Selenium must be needed.
3. Time-Consuming Test Case Creation: Creating test cases with Selenium can be time-intensive.
4. Setup Complexity Compared to Cypress: Setting up a test environment with Selenium is comparatively more complex than with Cypress.

Selenium Vs. Cypress: Choosing the Right Test Automation Tool

Selecting the ideal automation testing tool for your web application necessitates a thorough understanding of your project’s unique demands. It’s crucial to meticulously compare the distinctive features and limitations of Cypress and Selenium before arriving at a decision.

In your evaluation process, key considerations should include your development team’s skill level, the complexity of the web application, the necessity for cross-browser or mobile testing, and the availability of budget and resources. The choice between Cypress and Selenium should align with your testing objectives and needs. Below are some general pointers to assist you in determining the best fit for your project.

Selenium vs Cypress: Feature Differences

1. Primary Use Case:

1. Cypress: Front-end testing
2. Selenium: End-to-end testing

2. Supported Languages:

1. Cypress: Primarily JavaScript or TypeScript with plugin support for other languages
2. Selenium: Default support for major programming languages like Python, Java, C#, etc.

3. Installation/Setup:

1. Cypress: Simple installation using npm install cypress command (built on Node.js)
2. Selenium: Time-consuming setup involving Selenium and driver downloads and configuration

4. Supported Browsers:

1. Cypress: Chrome, Edge, and Firefox (current)
2. Selenium: Supports compatibility testing with all major web browsers, including Firefox, Chrome, Safari, and Edge

5. Speed and Efficiency:

1. Cypress: Faster due to direct browser execution
2. Selenium: Generally high-speed, but may not match Cypress in speed

6. Community and Integration:

1. Cypress: Growing community support, limited integration options
2. Selenium: Strong and well-established community support, seamless integration into numerous tools and platforms

7. Mobile Testing:

1. Cypress: Cannot perform mobile app testing
2. Selenium: Supports mobile app testing through Appium WebDriver API

8. License:

1. Both are open-source; Cypress is open-source and free to use, as is Selenium

Based on your project’s requirements regarding testing type, language preferences, ease of setup, browser compatibility, speed, community support, integration needs, mobile testing, and licensing preferences, you can make a well-informed choice between Cypress and Selenium.

Comparing Selenium vs Cypress: Understanding Architecture

Comprehending the architectural distinctions between Cypress and Selenium is pivotal in automation testing frameworks. These differences significantly influence the efficiency and effectiveness of your testing endeavors, guiding you to choose the framework that best aligns with your web application testing requirements.

Architecture:

• Cypress: Cypress adopts an end-to-end testing framework, executing tests directly within the browser, promoting efficiency in testing flow from start to end.
• Selenium: Selenium operates as a comprehensive web testing framework, interacting with the browser through a driver, enabling versatile testing capabilities.

Browser Interaction:

• Cypress: Cypress takes a direct approach, running tests within the browser, resulting in streamlined and rapid test execution.
• Selenium: Selenium interacts with the browser through a driver, providing a bridge for test commands and actions.

DOM Manipulation:

• Cypress: Cypress employs its specialized built-in DOM manipulation functions, simplifying and enhancing the manipulation process.
• Selenium: Selenium relies on JavaScript for DOM manipulation, utilizing standard language features for the same purpose.

Understanding these architectural variances empowers you to make an informed decision, selecting the framework that harmonizes with your web application’s unique testing demands, ultimately contributing to a robust and efficient testing process.

Selenium vs Cypress: A Comprehensive Comparison

The time has come to discern the superior automation testing tool. Selenium, a seasoned veteran since 2002, boasts a vast install base and enjoys unmatched popularity. In comparison, Cypress, a newcomer, is swiftly gaining traction for its swiftness in API, front-end, and end-to-end testing, thanks to its unique architecture. However, it’s pertinent to note that Cypress is currently limited to supporting only JavaScript.

Selenium’s strength lies in its multilingual support, yet its complexity poses a challenge for swift adaptation by developers and testers. Conversely, Cypress’s ease of learning and quick usability make it a preferred choice for automation testing primarily due to its simplified architecture. As a result, Cypress emerges as the victor, excelling in ease of use and speed. However, it can only partially replace Selenium or be used with it. Selenium remains indispensable, especially for extensive projects running thousands of tests.

Both frameworks necessitate considerable coding efforts, lack robust user administration or reporting, and often encounter challenges with element locator techniques, necessitating rework. This translates to significant time investment in authoring, maintaining tests, and supporting teams, and embracing low code tools aids in modernizing test automation, leveraging artificial intelligence to expedite test creation and reduce maintenance efforts, ultimately enabling efficient scaling. The time saved surpasses any associated licensing costs.

Choose Cypress if:

• Your application is a single-page application (SPA)
• You prefer a tool that is easy to set up and use
• Real-time reloading and time-travel debugging are priorities
• Stable, reliable tests with automatic waiting and retrying matter
• You are comfortable with JavaScript and do not require support for multiple programming languages

Choose Selenium if:

• Your application demands cross-browser testing or mobile device testing.
• You favor a more traditional, well-established web testing framework.
• Multilingual support (Java, Python, C#) is a necessity.
• A customizable testing tool or tailored test management is required.
• You are comfortable with a more complex, feature-rich testing tool and possess the resources and expertise to manage and maintain it.

In conclusion, both Cypress and Selenium are esteemed automation testing tools, each with its capabilities and features for web applications. To make an informed choice, it’s vital to understand the nuances of Cypress vs. Selenium and select the tool that aligns best with your project’s specific needs and requirements.

How HeadSpin Enhances Test Automation through Optimal Frameworks/Tools

1. Efficient Test Script Development and Management: HeadSpin employs leading automation tools to craft and organize test scripts effectively. These tools aid in creating robust, reusable scripts encompassing a wide array of test scenarios.
2. Comprehensive Cross-Platform Testing: Utilizing automation tools, HeadSpin ensures thorough testing across diverse platforms, including mobile devices, web browsers, and operating systems. This guarantees consistent behavior and extensive coverage across varying environments.
3. Enhanced Parallel Test Execution: HeadSpin optimizes testing speed and efficiency through automation tools that enable parallel test execution. Running multiple tests simultaneously drastically reduces overall test execution time.
4. Seamless Integration with CI/CD Pipelines: Automation tools seamlessly integrate into the continuous integration/continuous deployment (CI/CD) pipelines at HeadSpin, enabling automated testing at crucial development stages. This results in rapid feedback on changes and mitigates the risk of introducing defects.
5. Effective Test Data Management: Automation tools are pivotal in inefficient test data management at HeadSpin, offering capabilities to generate, manipulate, and maintain test data across various test scenarios. This strengthens the robustness of test cases.
6. Comprehensive Reporting and Analysis: HeadSpin harnesses AI/ML to generate detailed, insightful test reports. These reports furnish valuable metrics and analyses, enabling a thorough assessment of application quality, identifying bottlenecks, and data-driven decisions for continuous improvement.

Summing Up

When comparing Selenium vs Cypress, we recommend teams begin exploring Cypress to complement their existing Selenium scripts, enhancing overall test coverage and stability.

For those with a robust and stable Selenium suite covering adequate functionality, there’s no immediate need to switch tools. However, a Cypress proof of concept (POC) can provide a promising future solution for new projects.

Regardless of your choice between Selenium vs Cypress for cross-browser testing, HeadSpin elevates the testing experience. HeadSpin amplifies test coverage and enables scalable test automation by offering seamless integration with both Selenium and Cypress and robust end-to-end continuous testing support.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/selenium-vs-cypress-the-key-differences

Our strides in streaming platforms have been significant in today’s digital age, revolutionizing media delivery. As numerous service providers compete, achieving a seamless user experience and consistent streaming quality are paramount. Yet, building and launching an OTT platform demands substantial effort, exacerbated by the diverse array of devices used to access content. The pandemic has only intensified the demand for OTT applications, with users dedicating more time to streaming. Consequently, OTT service providers must ensure cross-platform compatibility and conduct rigorous testing pre-launch, ensuring a flawless experience, exceptional usability, and the platform’s ultimate success.

The Significance of OTT Reliability Testing in the Streaming Platform Realm

The burgeoning OTT services market prompts content owners to release many apps for mobile devices, TVs, and set-boxes. While numerous software development companies offer quick and cost-effective solutions, the challenge lies in launching a high-quality product that captivates subscribers and garners positive feedback in app stores.

Complete OTT solutions encompass critical components like content delivery, streaming service optimization, video hosting, and monetization management through advertising or subscriptions. This intricate software system demands precise construction at every step, necessitating robust software testing.

Here’s why leading companies prioritize OTT platform reliability testing within their Quality Assurance practices:

1. Ensuring Quality Across Diverse Platforms: If it has a screen, chances are it’s a potential platform for on-demand media streaming. Consumers now expect content to be accessible on various devices, from desktops and mobiles to tablets and gaming consoles. A robust OTT test automation strategy guarantees consistent streaming quality across all these platforms with each release.
2. Ensuring High-Quality Video Playback: Seamless video playback is paramount—delays or interruptions in content playback result in a rapid loss of audience interest. High-quality playback performance directly relates to sustained audience engagement. Your team must maintain this positive user experience through continuous OTT platform reliability testing.
3. Adaptability to Bandwidth Fluctuations: Ideal Wi-Fi access isn’t always guaranteed. Consumers expect a continuous streaming experience, even when top-notch internet bandwidth isn’t available. OTT test automation lets your team confirm that streamable content can gracefully adapt to varying bandwidth conditions.
4. Efficient Recovery from Network Failures: A non-existent streaming experience is a worst-case scenario. Your team needs a solid strategy for swift recovery during network downtimes, ensuring your consumers can still enjoy a quality user experience. Through OTT automation testing, your QA testers can validate the strength of your recovery plan and identify potential defects that could lead to network issues if left unaddressed.

What are the Varieties of OTT Platform Testing?

Testing is a crucial facet in ensuring the quality of a product. Let’s explore some of the essential types:

Functional Tests

• UI Testing: This assesses the frontend elements like buttons, forms, and links to ensure they function as intended.
• Feature Testing: It validates if the features outlined in client requirements are present and operational, identifying potential bugs or issues.
• API Testing: QA specialists conduct API tests to ensure seamless integration with databases, operating systems, libraries, and third-party services for a smooth user experience.
• Compatibility Testing: This ensures the product works seamlessly across various devices, operating systems, browser versions, and hardware specifications.
• Acceptance Testing: Users test the product from their perspective before release, providing valuable feedback to ensure functionality aligns with expectations.

Non-Functional Tests

• Performance Testing: Evaluates the product’s performance under different user loads and heavy traffic, aiding in understanding scalability and capacity.
• UX Testing: Focuses on design, usability, and overall user experience to ensure the product is user-friendly and visually appealing.
• Localization Testing: Assesses the product’s performance and capabilities from the perspective of diverse audiences with different languages and cultures.

Testing Post-Release

During this phase, several tests are typically conducted:

• Smoke Testing: A quick check to catch apparent errors following bug fixes or updates before further extensive testing.
• Regression Testing: Double-checking for bugs in new updates after code changes to ensure nothing slips through the cracks; often automated for efficiency.
• QA Audit: Independent companies conduct meticulous QA audits to evaluate product quality and performance, ensuring adherence to required standards.
• Business Analysis: Involves working with user feedback and monitoring trends to gauge product performance and identify areas for improvement, essential for informed decision-making and effective product scaling.

OTT Platform Reliability Testing: A Comprehensive Checklist

Testing OTT platforms, various devices, and testing approaches are essential to ensure an optimal user experience. Although different OTT platforms may have unique operational methods, they share a common goal. Here’s a concise checklist encompassing both standard and device-specific testing scenarios:

1. UI, UX & Functionality

• Test video recommendation algorithms by simulating regular user behavior.
• Verify key features like autoplay and skip intro for proper functionality.
• Validate watchlist functionality for saving and viewing videos later.
• Ensure seamless video playback continuation and easy differentiation between watched and unwatched videos.
• Test trailer autoplay features, allowing users to pause, mute, or unmute as needed.
• Confirm the ease of sharing video links with friends.

2. Streaming and Performance

• Check synchronization of audio and video with switchable subtitles.
• Experiment with language switches during streaming and monitor their impact on playback.
• Evaluate streaming quality across various connectivity options (Wi-Fi, LAN, Mobile Data).
• Confirm offline content playback availability without an Internet connection.
• Optimize video start time to stream promptly upon user initiation.
• Stress tests the platform’s stability under a load of concurrent users.

 3. Account

• Validate user access rights to subscribed content.
• Verify accurate tracking of subscription details, devices, billing information, and account management.
• Test multi-user profiles to ensure personalized watchlists, viewing histories, downloads, and recommendations.

 4. Security & Billing

• Ensure a secure payment process, supporting multiple payment options.
• Test various payment failure scenarios, providing appropriate guidance to users.
• Validate accurate storage and deletion of billing information.
• Verify the functionality of auto-renewal features and receipt downloads.
• Test Two-Factor Authentication for effectiveness.

 5. Cross-Platform Testing

• Test search functionality using voice commands for Smart TVs, ensuring usability with a remote.
• Evaluate OTT experience on mobile devices, considering interruptions like notifications.
• Assess user experience on desktop, focusing on browser and web application usage without compromising playback quality.

Following this comprehensive checklist, OTT platforms can ensure a seamless and satisfying user experience across various devices and platforms.

How to Ensure Reliability Through Testing of OTT Apps

Testing OTT apps for reliability is a complex endeavor involving scrutiny across various devices, browsers, and networks. Proficiency in diverse video streaming protocols is crucial to guarantee the platform’s expected performance. This comprehensive testing is indispensable for maintaining streaming quality and security.

A reliable testing process for OTT projects involves the following steps:

1. Test Planning and Architecture Design:

• Develop a comprehensive test plan based on requirements and industry standards.
• Design a testable architecture aligned with specified requirements and standards.

2. Automation of Testing:

• Automate the devised test plan to enhance efficiency and effectiveness.

3. Alpha Version Testing:

• Conduct automatic tests to evaluate the alpha version’s performance.
• Software Engineers enhance reliability based on the test report, resulting in a dependable alpha version for further testing.

4. Error Analysis and Correction:

• Skilled, Reliable Engineers thoroughly analyze error reports to identify root causes.
• Replenish reliable software standards, update the test plan, automate testing, correct errors, and deliver a reliable version for subsequent testing based on the problem analysis report.

Maintaining and enhancing reliability throughout the development process ensures the creation of robust OTT products, ultimately leading to a seamless streaming experience for end-users.

The Escalating Demand for OTT Testing Automation

Automation testing, a versatile approach applicable to many industries and software types, emerges as indispensable in OTT application testing. A typical OTT app is complex, supporting numerous media formats housing vast content, including stations, playlists, and terabytes of data. Moreover, it caters to diverse platforms and languages. The landscape is further compounded by the constant evolution of OTT software, necessitating regular testing for new functionalities.

Many OTT and media companies now rely on automated media streaming testing in response to these challenges. This strategic move enhances test coverage efficiency and optimizes resource utilization within manual testing departments. Expediting the debugging process paves the way for faster release cycles. Ultimately, this translates to a progressive, user-friendly, and stable solution, setting them apart.

While many tools and programming languages are available for OTT application testing automation, the key lies in the expertise of a precise and experienced automation QA team who can harness these tools effectively.

How HeadSpin’s Advanced OTT Platform Testing Enhances Digital Experiences for Global Media Companies

In the diverse landscape of OTT users accessing content through various devices, the HeadSpin Audio Video Platform stands as a robust tool for OTT platform reliability testing. It excels in cross-browser and device compatibility testing, making it an invaluable resource for conducting efficient OTT automation testing across OTT services and other media applications such as video conferences and gaming.

The unique strength of the HeadSpin Audio Visual Platform lies in its accessibility from a range of device locations, enabling comprehensive testing over different regions. This facilitates the identification of localization issues and a thorough comparison of results across regions and with previous application versions.

Here are HeadSpin’s capabilities for simplifying your OTT and media testing journey:

1. Diverse Device Testing: Utilize the AV Platform to effortlessly conduct tests on a wide array of OTT media devices. This ensures seamless content delivery to users, regardless of their chosen device.
2. Robust Content Security: Content security is of utmost importance. HeadSpin’s AV Platform allows you to test DRM-protected content, preserving the integrity of your valuable media while delivering a smooth viewing experience.
3. Thorough Audio Testing: In today’s digital landscape, voice-activated and speaker-based applications are integral. HeadSpin’s AV Platform facilitates comprehensive testing of these use cases, ensuring glitch-free audio interactions.
4. Effortless Remote Control: Simplify the testing process by executing commands on the setup box through the HeadSpin UI. This enables the easy capture of Quality of Experience (QoE) and streaming performance Key Performance Indicators (KPIs).
5. AI-Driven Insights: Access in-depth insights into perceptual video quality KPIs and AI-generated issue cards. Understand the nuances of video and audio performance to make informed, data-driven decisions for optimization.
6. Streamlined Playback and Analysis: Post-testing, effortlessly playback and analyze media content using our advanced video and audio analysis tools, granting you complete control over the quality of your content.

Summing Up

In conclusion, OTT platform testing is a critical process in guaranteeing the reliability and excellence of streaming services. By identifying and resolving issues such as buffering, freezing, and compatibility glitches, OTT testing significantly enhances user experience and elevates customer satisfaction. It also plays a vital role in tailoring content for diverse devices and network configurations while ensuring adherence to industry standards and regulations.

Looking ahead, the future of OTT testing is expected to be shaped by increased automation and integration with AI and machine learning technologies. This transformation promises heightened testing effectiveness and accuracy, expediting problem detection and enriching the user experience. In this evolving landscape, the success of OTT networks will increasingly hinge on robust OTT testing practices, particularly with the escalating demand for top-notch streaming services.

Choose HeadSpin’s AI-driven Platform for unparalleled reliability testing of your OTT platform, ensuring perfect streaming experiences for your audience.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/ott-reliability-testing-and-transformation-of-media

Did you know that one of the world’s leading platforms for gamers experienced an impressive 83% increase in viewership in a single year after the pandemic hit? 

Today, gaming is proving to be more than just an entertainment option, as its role has significantly evolved during the pandemic. While it was often associated with leisure activities confined to people’s bedrooms, gaming is now demonstrating its true value in various aspects of life, including the classroom. Amid this boom in usage, ensuring optimal gaming experience is a top priority for gaming companies to maintain a superior brand reputation. 

Nobody wants their players to experience disappointment at any point in their gaming journey. While impressive graphics, animations, and rich media can make a game attractive, poor performance frustrates players the most.

Gamers expect a consistent and seamless gaming experience, highlighting the importance of performance testing for gaming apps.

What is performance testing, and why do you need it?

Large-scale gaming applications need to undergo performance testing to be able to deliver expected outcomes to the end users. Effective software testing platforms for gaming apps help tune the servers for optimal performance to obtain a realistic assessment of the game’s performance. 

Performance testing allows you to obtain crucial information on how the games perform in terms of responsiveness, stability, and refresh rates under specific user interaction patterns or certain workloads, and much more. Without efficient performance testing, the app might be exposed to a myriad of bugs, defects, security threats, and other issues with speed and reliability on release. Performance testing effectively reduces productivity costs by reducing system crashes, discovering bottlenecks, and ensuring that the software is fast, stable, and can handle multiple users.

Crucial metrics for testing the performance of game apps

Effective performance testing focuses on three broad areas: speed, stability, and stability. Testing the performance effectively requires several metrics that help monitor and enhance the quality of performance easily. Following are some metrics that game testers should focus on:

• Response time – This helps calculate the total time between the sending of a request to the time of receiving the response. A faster response time indicates higher performance. 
• Average load time – This is an essential metric from the user’s perspective. The average load time is the measurement of the time taken to deliver each request. 
• Latency – Also known as the wait metric in performance testing, latency helps indicate the time taken for the first response. It is crucial to measure the time consumed before receiving the first byte as a response that helps measure the performance levels of the web servers.
• Connections per speed – This metric provides information regarding the TCP/IP connections, which are opened and closed every second. In case the number of connections per second is lesser than the hit per second, it reflects effective performance.
• Concurrent users – The number of users using the applications simultaneously significantly impacts the app’s overall performance. The concurrent user metrics assist in measuring the number of active users at a point in time, indicating the engagement of the users. 
• DAU/MAU(Daily active users/monthly active users) – This refers to the ratio of active users who play each day over the total number of monthly active users. This is also known as the stickiness factor.
• Session – Each time any user opens an app, that counts as a session. The focus here lies on the average number of sessions per DAU.
• Download rank – This refers to the rank of the game in a specific app store by monthly game downloads.
• Retention – This is a very crucial metric for an android game tester for any free game. For calculating retention, it is required to separate the users into cohorts based on the particular day the app was downloaded.
• Throughput – This is measured by kilobytes per second that show the amount of bandwidth utilized during the test.
• CPU utilization – This is a measure of the time required by the CPU to process requests.
• Memory utilization – This refers to the amount of memory needed to process the request.

Types of performance testing for the gaming applications

It is important that QA testers understand the different forms of performance testing to be able to identify the right one for their app.

1. Load testing

Load testing measures the system performance as the workload increases. The workload could refer to concurrent users or transactions. For this test, the system is monitored to measure the response time and system staying power with the increasing workload.

In the case of large-scale gaming apps, load testing helps determine the maximum number of players that can play on a server or if the gaming app is communicating effectively with your server or the memory used by the software, and many more. Load testing can assist in checking the app against these scenarios.

When a specific load is exposed to the game and infrastructure, the developer can understand the possible bottlenecks and identify the parts of the infrastructure that require optimization. Load testing might vary from basic server upload/download function to database connection and queries and various inputs provided to the game/infrastructure.

For instance: If the game requires you to pull something from a back-end server and suddenly 200 plus devices do that in the same WiFi network, such a scenario can offer insightful information about the game’s capability during this kind of scenario. It can also assist in providing deep insights into hardware capabilities and how many resources are required for the games.

2. Interrupt testing

Interruptions can be very critical for any application. Imagine getting a call, incoming SMS, network connection loss, app update reminders, or any other notifications that might get in the way of your gameplay. Therefore, it is essential to test for interruptions to test the app behavior and response during and after the unexpected disturbance.

3. Stress testing

This type of testing technique determines the robustness of the software to measure system performance beyond the parameters of normal working conditions. Stress testing emphasizes robustness, availability, and error handling under a heavy load rather than what is correct behavior under normal situations. Stress testing commonly involves metrics like transaction response time, transactions per second, pages per second, failure of connection, and many more.

4. Endurance testing 

This is crucial non-functional testing that is executed to check if the software system can seamlessly sustain under a huge load continued over a long period of time. Through this technique, the game is left running for a prolonged period in different modes of operation, for instance, idling paused or at the title screen. Endurance testing, also known as soak testing, critical metrics like CPU, memory, and FPS can be monitored to detect potential leaks.

For example — Assume a game has commenced and the character is made to stand idle for 24 hours. With this technique, testers can detect crashes brought on by memory leaks and other errors in the game engine.

5. Spike testing 

Spike testing is a technique leveraged to test apps with extreme increments and decrements in traffic loads. The main aim of spike testing is to evaluate the behavior or response of the app to changing user load and determine the recovery time.

This form of testing is executed by suddenly increasing the load generated by a large number of users, inputs, data, or other types of stimuli. This is a periodical practice that helps determine if a dramatic change in load leads to any errors or issues to the game and its infrastructure.

Things to keep in mind for executing performance tests for gaming apps

– Ensure writing and conducting performance tests in whichever types of tests make the optimal use for the game. Make sure to stress the game and the entire gaming infrastructure as well. This involves back-end servers and integration with social media and other third-party entities.

– It is essential to input every element, which helps to investigate further what might happen to the game. This allows monitoring it in real-time during the testing. Further, the logs will maintain all the vital information that can be used to compare later with build-over-build to assess how things have regressed with the new version of the game.

– Leverage real mobile devices and check for the following aspects:

• The game’s graphics and UI—whether the game display shows properly on all devices
• If the UI and layout and other graphical elements like icons, and buttons, display properly across all devices
• Ensuring that the usability of controls is identical in all devices
• Ensuring the proper flow of the animations
• Checking whether the game progress flows seamlessly
• Checking whether the screen resolution and orientation are correct for the devices and similar other aspects.

Gaming app testing is a crucial yet complex task for the testers and developers as it needs to cater to multiple requirements. Testing the performance of these apps can be very different when compared to other applications. The success of the apps not only depends on rendering good user experiences but also on the time engagement of users with the applications. Additionally, gaming apps have transformed significantly today. Single-player online board games that ruled once upon a time are not the only popular games today with emerging social games with enhanced graphics, multiplayer features, interactive interfaces, and much more. These social games need extensive testing not only for intricate graphics and smooth gameplay tasks but other features like real-time response, communication, AR VR functionalities, and many more. Following are some of the popular types of games and their requirements:

• Massively Multiplayer Online Games (MMOGs) – Games like Roblox and Minecraft allow thousands of players from around the world to log in and play together. Gamers can choose how much interaction they wish to engage in. These games need to ensure the smooth functioning of the interactive chatting features among players irrespective of the number of players. 
• Player versus Environment (PvE) – Player versus environment (PvE) are video games where the players compete against artificial intelligence instead of other players. The players usually compete against AI-controlled opponents with varying difficulties as the game progresses. These games cover a wide range of titles, from adventure and action to role-playing games (RPGs) that require seamless video quality and smooth transitions, with no latency, network jitters, or slowdowns.‍
• Multiplayer Online Battle Arena (MOBA) Games – MOBA is a popular sub-genre of real-time strategy games. These games involve teams competing against each other on a predefined battlefield. Every player in this game controls a single virtual character and possesses distinctive abilities that enhance over the course of the gameplay and help improve the team’s performance. These types of games involve a large number of players and require smooth communication and flawless audio and video quality across the game journey to avoid any disruptions in the gameplay.

How to test game performance

Testing game performance is crucial for ensuring a seamless gaming experience, especially for large-scale gaming applications. Here are steps to effectively test game performance:

Define performance metrics

Before starting performance testing, defining the key performance metrics that need to be measured is essential. Common metrics include:

• Frame Rate (FPS): Measures the smoothness of the game visuals.
• Load Time: The time it takes for the game to load.
• Latency: The delay between a user’s action and the game’s response.
• Memory Usage: The amount of memory the game consumes during different states.
• Battery Usage: The game’s impact on battery life, especially for mobile games.

Create test scenarios

Develop realistic test scenarios that reflect actual gameplay. These scenarios should cover:

• Normal Gameplay: Regular playthrough to observe performance under standard conditions.
• Stress Testing: Simulating peak usage with many players or high-intensity scenes.
• Network Conditions: Testing under various network conditions (3G, 4G, Wi-Fi) to measure latency and data usage.

Conduct automated and manual testing

Combine automated and manual testing to get a complete picture of game performance. Automated tests help identify performance issues quickly and consistently, while manual testing provides insights into the user experience.

Monitor and analyze results

Monitoring tools are used to collect data during the test. Analyze this data to identify performance bottlenecks. Key aspects to analyze include:

• CPU and GPU Usage: High usage may indicate areas for optimization.
• Memory Leaks: Identify and fix memory leaks to improve performance and stability.
• Network Traffic: Optimize data usage to reduce latency and improve responsiveness.

Optimize and retest

Optimize the game code, assets, and configurations based on the analysis. After making changes, retest the game to ensure the optimizations have resolved the performance issues without introducing new problems.

By following these steps, you can ensure that your large-scale gaming application provides players a smooth, responsive, and enjoyable experience, regardless of the device or network conditions.

Extensive platform diversity — a significant complexity for consistent game testing 

Testing game performance across different platforms introduces a complex and dynamic dimension to quality assurance. Game developers must address variations in hardware capabilities, operating systems, and user interfaces, all while maintaining gameplay consistency and high-performance standards. Cross-platform game performance testing is crucial to ensure that gamers on various devices enjoy a seamless and engaging experience, free from glitches, lags, or other platform-specific issues. This requires a comprehensive testing approach that accounts for the unique characteristics of each platform while striving for a unified, top-tier gaming experience.

Though it is critical to test game performance across platforms of various types, it is often challenging due to the variables.

Cross-platform testing involves evaluating how a game performs on various hardware configurations and operating systems, such as Windows, macOS, iOS, Android, Xbox, PlayStation, and more. Here are some key aspects that make it challenging to test game performance across platforms: 

● Hardware variability 

Different platforms have varying hardware capabilities, from CPU and GPU power to memory and storage. Testing across platforms helps identify performance discrepancies and ensures that the game functions smoothly on all devices.

● Screen resolutions

Games should adapt to different screen resolutions and aspect ratios, from high-definition monitors to smartphone screens. Testing helps ensure that graphics and user interfaces are appropriately scaled and rendered.

● Consistency 

Gamers expect consistent gameplay experiences, regardless of the platform they use. Cross-platform game performance testing helps maintain a uniform gaming experience, preventing unfair advantages or disadvantages for players on different devices.

● Bug identification

Testing helps uncover platform-specific bugs or issues that may affect gameplay. Identifying and addressing these problems ensures a bug-free gaming experience for all players.

Additionally, with a rising emphasis on assistive gaming, there are several other considerations to be involved while testing gaming apps across different mediums. 

Assistive gaming, also known as accessibility gaming, leverages adaptive technology to cater to individuals with a range of disabilities, including low vision, blurred vision, blindness, color perception challenges, speech and hearing impairments, as well as cognitive and motor mobility limitations.

Popular games like Cardinal Direction (CD) and Tower of London (TOL) have been adapted to accommodate visually impaired users. These adaptations typically replace visual stimuli with audio inputs, creating a more inclusive gaming experience.

When testing such games, a video game tester should pay close attention to the following aspects:

1. Color patterns: Ensure that colors flash in a discernible pattern, accompanied by distinct audible tones for each color.
2. Auditory accompaniment: Each color should have an associated audible tone to aid players in distinguishing between them.
3. Visual data description: Provide descriptive text for visual elements within the game so that visually impaired players can access information through screen readers effectively.
4. Three-dimensional sound: Implement three-dimensional audio and spatialized sound to allow players to navigate the game world using touchscreen controls while receiving audio cues from different directions.

How can HeadSpin help you deliver flawless gaming experiences?

Global gaming companies are leveraging HeadSpin to deliver unparalleled gaming experiences for the gamers by using advanced ML models and holistic testing suites. The key benefits that HeadSpin offers gaming companies include—

• End-to-end testing — HeadSpin assists in end-to-end testing for all the gameplay scenarios to help measure gaming experiences across real user conditions with real devices and consoles, carrier networks, locations, and localized content. Additionally, create, execute and automate tests easily on real devices with HeadSpin’s capability to effectively support multiple automation frameworks and easy integration with the existing frameworks.
• Cross-platform compatibility — HeadSpin’s testing capabilities extend to cross-browser and cross-platform scenarios. This means gaming companies can confidently verify their gaming experiences on various device types and across different global locations. Ensuring cross-platform compatibility maximizes your reach and user base without compromising the quality and performance of your games.
• Critical KPIs — HeadSpin’s gaming solution enables measuring the KPIs essential for understanding the user experience issues and improving the gamer’s engagement with the app, including memory usage, battery usage, audio/video mean opinion score, frame rate, launch time, and much more. Mean Opinion Score (MOS), an efficient way to quantify the subjective perception of video content through a single number, can be captured with HeadSpin, and the MOS time series can be further paired with the HeadSpin Poor Quality Issue Card, where the UI will surface perceptual video quality issue regions to the time series. The video quality metrics in total enable a better understanding of which metrics have the biggest impact on the user quality of experience.
• Other critical factors impacting gameplay experiences — With HeadSpin, test and monitor network latency and in-app transaction performance, run multiplayer testing, and measure the performance of in-game advertising campaigns. 

How HeadSpin assisted a leading game developer in delivering flawless gaming experiences across the globe

While attempting to keep an engaged customer base by ensuring a seamless gaming experience, this client was faced with the challenge of testing users’ experiences in diverse locations across the world. 

HeadSpin’s unique testing platform enabled the company to test the real-life performance of their apps anywhere with thousands of real SIM-card-equipped devices. The platform helped diagnose performance and customer experience issues of critical user flows and identify the highest priority issues. With HeadSpin, the client team could also leverage the recorded UX sessions to understand better the UX aspects that are challenging to quantify. 

HeadSpin’s gaming solution further enabled the client to make changes to fine-tune the in-app purchases and provide a streamlined purchase experience.

In a nutshell.

Performance testing is a key to ensuring seamless gaming experiences for the users with constant progress, smooth gameplay, and consistent performance across the entire game journey. Offering compelling experiences for the target audience helps enhance the brand reputation and win loyalty across the clientele. It is crucial for the testers and developers to execute thorough performance testing across every essential metric and diverse gameplay scenarios to ensure delivering unparalleled gaming performance.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/performance-testing-for-large-scale-gaming-applications

Testing is pivotal in the Software Development Life Cycle (SDLC). Ensuring the seamless functionality of every software component is imperative to meet both business and user expectations. Amid the diverse array of tests and testing methodologies, it’s not uncommon for regression testing and unit testing to be conflated, leading to confusion. Distinguishing these two vital testing types is paramount for the success of the SDLC and, ultimately, for delivering high-quality software. 

This blog delves into the essential details, advantages, and key distinctions between regression and unit testing to shed light on their unique roles in software development.

Understanding Regression Testing

In the world of software development, haste is seldom advisable. Every step and process holds significance; none can be overlooked.

Regression testing adopts an intuitive approach to evaluating software products. Typically conducted before or after system modifications, it is a versatile solution for addressing various software glitches. This is where regression testing intersects with unit testing.

While unit testing focuses on isolated components, regression testing is performed with other critical test cases. It is indispensable and beneficial for any system. Neglecting it can lead to significant setbacks for a company or enterprise.

For instance, consider designing a sign-in page for an online shop. Users must access it using their credentials, which can make them vulnerable. Regression testing is pivotal in addressing issues like cyberattacks gaining unauthorized access or legitimate users facing restrictions.

Regression testing ensures the security and functionality of the feature and other interconnected components, safeguarding users from potential disruptions.

Regression Testing Techniques

1. Complete Retest: This technique is the go-to when substantial changes are required in your application’s core code. It thoroughly examines all components, encompassing any recent software alterations. While comprehensive, it’s also resource-intensive and time-consuming. Reserve this method for in-depth regression testing.
2. Selective Regression Testing: This technique comes into play when dealing with minor software alterations. It allows you to focus on a specific module with limited changes. With this approach, you can assess how the existing software behaves with newly added or removed features while maintaining a history of test results.
3. Prioritization of Test Cases: Ideal for regression testing, this technique prioritizes test cases based on their criticality and risk to the product. High-priority cases take precedence, followed by medium and low-priority ones. This method ensures that the most critical aspects are thoroughly examined first, streamlining the testing process.

Exploring Unit Testing

Unit testing is a critical practice in software development, focusing on evaluating the source code early in the development life cycle. It involves scrutinizing individual units of code to ensure high-quality programming. As this test type delves into the internal workings of the software, it falls under the category of white-box testing. Traditionally, software developers were primarily responsible for conducting unit tests within companies due to their familiarity with the codebase.

Since its scope is limited to a specific portion of the code, unit testing is quick and straightforward. Moreover, it plays a vital role in cost reduction by addressing defects and bugs in the early stages of development when the cost of rectification is considerably lower compared to later stages.

An additional advantage of unit testing is its automation, making it an efficient and reliable testing methodology. This practice is fundamental, especially for teams following agile methodologies, enhancing programming efficiency and ensuring the development of robust software products.

Unit Testing Techniques

1. Black Box Testing: Commonly referred to as Behavioral testing, Black Box Testing is centered on software requirements and specifications. It emphasizes the application’s input and output without delving into its internal code implementation. It symbolizes the software system being tested, and testers execute evaluations based on provided inputs and expected outcomes.
2. White Box Testing is an internal approach to enhance security, usability, and design. Testers have visibility into the code during this testing type, allowing them to verify the input-output flow and understand the software’s internal structure. The name “White Box” originates from this ability to perceive the software’s inner workings, providing insights into its architecture and logic.
3. Gray Box Testing: Gray Box Testing balances Black Box and White Box Testing methodologies. It seeks to identify defects that might arise if the code structure isn’t functioning as intended. Testers possess some knowledge about the application’s internal structure, enabling a combined approach that harnesses the benefits of both black-box and white-box testing. This fusion enhances the testing process and contributes to an overall improvement in product quality.

How Regression Testing and Unit Testing Are Applied

Understanding the distinctive use cases of regression and unit testing is vital, considering their theoretical similarities often blur the lines between them. Despite seeming akin, they serve unique purposes, acting as proactive security measures in software development to preempt potential issues.

Applications of Regression Testing

• Malfunction Resolution: Utilized when a system reports a malfunction or code interruption, ensuring written code integrity.
• Conversion Test Method: Moving the program to a different environment and validating accurate integration, primarily focusing on modifications in the new environment.

Applications of Unit Testing

• Mock Object Utilization: Verifying specific parts of code absent in the main program by using mock objects, ensuring comprehensive code coverage.
• Continuous Code Validation: Performing unit tests during code writing, aiding in early error detection and seamless integration of new components.

Distinguishing Regression and Unit Testing

Understanding the differences between regression testing and unit testing is paramount for maintaining the robustness of any software. Although both serve the purpose of vulnerability scanning to detect threats, they vary in terms of their test objectives, stages in the development process, and the scope of the code they cover. Let’s delve into these distinctions to grasp their roles better.

Difference in Test Objectives:

• While similar, Unit and regression testing have distinct targets within your code. Unit testing, conducted by programmers, assesses individual components, validating the correct functionality of each variable, function, and object.
• On the other hand, regression testing (often termed QA testing) occurs after programmers complete work on specific features. It acts as a system-wide check, ensuring untouched components function as expected. While unit tests provide the precision of individual functions and variables, regression tests collaborate to ascertain that the entire system functions optimally.

Difference in the Development Phase:

• The timing of unit and regression tests sets them apart. Unit tests are conducted during the development phase, where developers run them after implementing changes to confirm no adverse impacts.
• Conversely, regression testing is performed before the feature’s production release. It comprises unit tests, integration tests, and various other testing types. Testers are responsible for executing regression testing. Automated regression testing, a key step in continuous integration/continuous delivery, quickly detects if recent code changes have disrupted the existing code.

Difference in Code Coverage:

• A unit test concentrates on a single unit, method, or function, examining one element at a time. It doesn’t account for how these units interact, necessitating integration tests. This approach provides swift feedback due to its focused testing nature.
• In contrast, regression tests validate if alterations to existing functionalities have affected other parts of the system by testing against predefined scenarios, ensuring correct integration of units. Given the comprehensive testing involved, it generally consumes more time.

What Tools Facilitate Regression and Unit Testing

Regression Testing Tools

• Selenium: A widely recognized framework for web applications, Selenium supports multiple programming languages like C#, Java, and Python. It excels in automating web browser tests, ensuring validation and reliability. Selenium is praised for its high code readability, code reusability, and cost-effective script maintenance.
• Testsigma: An automation testing platform leveraging Natural Language Processing (NLP), Testsigma enables seamless automation of tests across web, mobile, desktop, and APIs. Its user-friendly interface allows for test automation with the ease of crafting sentences in English.
• UFT testing (Quick Test Professional – QTP): Renowned for its activity recorder feature, UFT testing allows users to record user actions and transform them into scripts, streamlining the testing process effectively.
• Subject7: A cloud-native test automation platform, Subject7 integrates all testing procedures into a unified platform, facilitating efficient and centralized testing operations.
• Cerberus Testing: An open-source and low-code test automation platform, Cerberus Testing offers support for various testing domains, including Web, Mobile, API, Desktop, and Database testing. It simplifies test automation while ensuring flexibility and effectiveness.

Unit Testing Tools

• NUnit: A well-established unit testing tool primarily written in C#, now compatible with all .NET languages. It offers extensive features, including support for data-driven tests and various platforms like Silverlight, Xamarin mobile, and .NET core.
• Mockito: An open-source tool renowned for simplifying the development of testable applications. Mockito facilitates using external dependencies in code, allowing developers to create mocks and manage return values and exceptions effectively.
• Emma: An open-source Java-based unit testing tool ideal for high-scale enterprise software. What sets Emma apart is its capability to provide code coverage to individual developers at no cost, promoting fast and effective testing of applications or software.
• Quilt HTTP: A versatile Java software development tool that is a cross-platform solution. In unit testing, Quilt HTTP aids in measuring coverage of Java programs, navigating control flow graphs, and streamlining the reporting process for comprehensive insights.

Streamlining Regression & Unit Testing with HeadSpin’s Advanced Testing Solution

HeadSpin Regression Platform stands as a reliable tool crafted for automated testing of both web and mobile applications. It aids developers in swiftly identifying and addressing regressions before the product goes live. The platform integrates with various development tools and supports multiple browsers and operating systems, providing a versatile regression testing option.

HeadSpin offers valuable insights into software application performance and user experience in unit testing. Leveraging metrics such as packet-level network data and device KPIs, the AI-based Platform provides actionable insights for organizations. These insights encompass issue cards, time series, session videos, and packet-level details, all crucial for enhancing application performance.

Key Features of HeadSpin’s Advanced Testing Solution:

• Automated Regression Testing: Offers tools and frameworks for automated regression tests, ensuring rapid retesting of applications after changes or updates without affecting existing functionality.
• End-to-end Automated Unit Testing on Real Devices: Enables end-to-end automated unit testing of software applications and mobile apps, promoting collaboration for product management and release efficiencies.
• Parallel Testing: Enables simultaneous testing on different devices or platforms, significantly expediting the regression testing.
• Test Reporting and Analytics: Provides comprehensive reports and analytics on test results, promptly identifying regression issues.
• Data Science and AI for Root Cause Analysis: Utilizes data science and AI to analyze degradation across new app builds, OS releases, feature additions, and locations, aiding in root cause analysis and error resolution.
• Integration with CI/CD Pipelines: Seamlessly integrates with continuous integration/continuous deployment (CI/CD) pipelines, automating testing during the development process and fostering a smooth workflow.

HeadSpin’s advanced testing solution enhances efficiency, reduces time-to-market, and ensures a competitive edge within the dynamic app landscape.

Conclusion

Unit and regression testing are vital pillars in the software development journey. Regular execution of these tests is key to minimizing bugs and refining code quality.

Regression testing conducted post-unit testing before a software release, ensures system integrity despite changes. On the other hand, unit testing meticulously validates new functions, ensuring precise code execution.

HeadSpin employs a data-driven approach, offering regression insights that empower professionals to monitor and adapt to application changes effectively. With AI-powered regression intelligence, HeadSpin facilitates insightful comparisons across builds, locations, and more, further enhancing software development and quality assurance.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/gaining-insight-into-regression-and-unit-testing

Ensuring seamless user experience across various platforms is of significant importance in this highly competitive, customer-centric era. Usability testing emerges as a critical component in this equation, offering invaluable insights by placing actual users at the center of the evaluation process. Whether it’s the straightforward analysis of session recordings or the in-depth examination facilitated by advanced eye-tracking labs, the essence of usability testing remains consistent: to understand user behavior, reactions, and potential obstacles. By emphasizing real user interactions, developers and businesses can fine-tune their software offerings, ensuring not just functionality but an experience that is efficient, effective, and deeply resonant with user needs and expectations.

What is usability testing?

Usability testing refers to assessing how simple a design is to use by observing how users interact with a product. This enables researchers to spot design flaws and user engagement capabilities, like whether or not users enjoy the product and if they find it easy to use. 

While designing a product, usability testing allows researchers to assess a product’s usability with larger amounts of data and find solutions to any issues that may arise. 

Usability testing can be performed at different stages to test each iteration of a product, from the initial development to its release. This helps researchers to address problems early on when they are more manageable.

Why should enterprises focus on usability testing?

● Saves significant time

Usability testing at the prototype stage is a game-changer. It’s like reading a chapter of a book before publishing. By doing this, you pinpoint issues early, preventing costly post-launch fixes. It ensures that product managers, designers, and developers are all on the same page from day one, eliminating guesswork. It’s not about working harder; it’s about working smarter, which can lead to saving immense time overall.

● Helps validate features based on hard data

Launching a product often stems from preliminary research. However, even with preparation, expectations can sometimes misalign with actual user needs. A robust usability testing platform primarily helps substantiate your product strategy through concrete data and real-world feedback. Studies indicate that a significant percentage of developed software features see little to no use. To ensure efficient resource allocation, it’s imperative to ascertain the genuine demand for the features you’re designing. Usability testing provides the concrete data needed, facilitating informed decisions for product development trajectories.

● Reduces costs and improves savings

Usability testing is a strategic investment, delivering significant financial returns in software development. Early-stage testing identifies and resolves potential user issues, preventing expensive rectifications during later stages. The immediate benefit? Saving on development hours and reducing the need for rapid, last-minute corrections.

Proactive testing further enables teams to discern the most valued product features, aligning development focus with user preferences and business objectives. Using actual user feedback to guide development decisions minimizes risks of costly feature modifications in the future, ensuring a streamlined build process.

The scalability of a usability testing platform is noteworthy. Even with constrained budgets, a single round of tests with just five users can uncover most usability challenges.

● Helps ensure the product success before market entry

Usability testing serves as a pivotal step in gauging your product’s real-world performance and potential acceptance. Through this process, you can validate initial assumptions about the product’s utility and relevance to the user. It helps preemptively spot issues, averting costly post-launch fixes. Furthermore, the ease of use is directly proportional to adoption rates; a user-friendly product is likely to see higher acceptance. Simply put, if a product isn’t intuitive or fails to address its intended problem, its market uptake will falter. With usability testing, you gain foresight into its anticipated market performance.

Exploring different usability testing methods

1. Quantitative and qualitative approaches

The quantitative angle of usability testing zeroes in on hard numbers and stats—think metrics like the time a user spends on a particular task. This method leans on concrete data points to affirm design choices.

Conversely, qualitative testing is all about diving deep into the user’s journey. It’s less about the “how many” and more about the “why” behind user actions. By closely watching and listening to feedback, we gain a clearer understanding of user preferences and pain points.

2. The setting: Remote or face-to-face?

Remote Sessions: Modern and efficient, remote tests cut across geographical boundaries. Participants and testers connect virtually, making this method both practical and cost-effective.

In-Person Sessions: There’s something about being in the same room. Face-to-face tests allow for a more intimate understanding, as we can pick up on subtle cues like body language, which sometimes communicate more than words. 

3. Paper Prototype Testing

One of the most traditional approaches to usability testing, paper prototype testing, involves having a dry run of test execution, drawing prototypes and hand sketching.

This test aims to discuss the flow of the product by drawing it on paper and using this platform to consider all possible scenarios, outcomes, conditions, and inputs. This type of testing is best to eliminate primary issues in the product, and organizations observe it across all projects. Paper prototype testing helps put thoughts down in writing, assisting teams to have clarity on the execution process. 

The project team usually runs the paper prototype tests and brings the best results in the earlier stages of testing. It is a cheaper method of testing. It may not be the most effective as it does not have the user at its core. Additionally, it is also time-consuming, which can lead to negligence.

4. Automated Usability Testing

Developers run this test by writing automation scripts. Automation usability testing involves writing scripts for automation, triggering, and executing said test scripts. Once developers execute the tests, they record the results and submit them for analysis. 

To perform this test, organizations must hire a resource if they don’t already have one who can build effective automation frameworks and write test scripts.

Jonathan Lipps, the project lead for the Appium Mobile Automation Platform and the Director, Automation Technologies at HeadSpin, is a key figure at HeadSpin University. He helps aspiring individuals who wish to learn automation through courses at the University. So, if you want to train your team in automation, you can get started here. Automated usability testing is cost-effective and gives excellent results as there is less chance for human error.

What is NOT usability testing?

Usability testing holds a specific niche within the spectrum of UX research methods, and it’s crucial to distinguish its unique role from other prevalent research strategies. Let’s clarify what usability testing is not:

• A/B testing: This method involves comparing two versions of a web page for determining which performs better. It mainly focuses on existing users, contrasting with usability testing that typically engages a representative target audience unfamiliar with the product.
• Usability inspections: Unlike usability testing, which revolves around real users’ interactions, usability inspections lean on expert evaluations. Techniques such as heuristic evaluations, cognitive walkthroughs, and pluralistic walkthroughs are employed by specialists to assess the product’s usability.
• Surveys: While surveys are instrumental in gathering user feedback, they lack the observational component inherent in usability tests. Surveys don’t permit hands-on, direct interaction with the product under controlled scenarios, which is pivotal in usability testing.

By drawing these distinctions, we can ensure the right research approach is chosen for the intended purpose, maintaining the integrity and specificity of usability testing.

Different stages of usability testing

• Planning: This phase focuses on defining the objectives of the usability test. During planning, clear goals are set, and questions like “What are we testing?” and “Which method will we use?” are answered to create a structured roadmap.
• Recruiting: Participants for the test are selected in this phase. The number of participants and facilitators depends on the project’s complexity and available budget.
• Execution: Under the guidance of a facilitator, participants carry out the usability test, adhering to the pre-defined scenarios and parameters.
• Data analysis: After testing concludes, the collected data is analyzed to identify patterns and areas for improvement.
• Reporting: Feedback from the test is documented and shared with the development teams. This phase involves discussions on necessary improvements and the subsequent action steps.

Factors To Consider for UX Testing

First and foremost, the earlier you test, the better it is to prevent risk, wastage of time, and save the project budget.

1. Test early; test often. This method helps avoid wasting time, saves budget, and prevents risk. 
2. Developers can handle areas that need improvement seamlessly if testing starts early.
3. During earlier stages, ensure your team of users has a good idea of your product.
4. Understanding the target audience is crucial for product development; this means understanding who they are, their needs, and whether your product satisfies them.
5. Identify new ways of using your product that users might look to in the future.
6. Have a sit down with your development team and understand areas of improvement.

Remember to consider the app’s rate of response and performance after the mobile usability test.

How can usability testing help your website?

What is website usability testing?

Website usability testing involves real users navigating through a website to assess its functionality, ease of use, and overall user experience, ensuring that the site meets its intended purpose effectively and efficiently.

Understanding the efficacy of your website’s interface isn’t merely about aesthetics or functionality in isolation; it’s about how users interact with and experience it. Here’s why web usability testing is indispensable at every stage:

• Early engagement: Even in the prototype phase, testing provides invaluable insights. By gauging real-time user interactions, you can identify challenges and address them proactively, ensuring that foundational designs are robust.
• Post-implementation review: After deployment, a revisit via usability testing ensures the platform behaves as intended. Is the user experience seamless? Does it resonate with the expected utility?
• Simplifying complex processes: For intricate sequences, say, an online checkout process, usability testing ensures the procedure is intuitive, minimizing user friction.
• Interpreting analytical data: There are times when quantitative data points out an anomaly, but the cause remains elusive. Usability testing often illuminates the reasons behind such patterns, offering context to raw numbers.
• Attention to detail: Minor oversights, be it broken links or grammatical nuances, can be caught and corrected through meticulous usability testing. It serves as a final quality assurance checkpoint.

Five Best Practices for Usability Testing

There are many aspects to consider while running usability tests. It can get difficult to remember what is essential with so much to do. Here are five of the best practices you can consider:

1. Ensure consent before you start the test

It is essential to get consent from your users while running usability tests. At the beginning of the test, it is necessary to get the user’s consent to record the test and its results. Once you complete conducting the test, you will need their consent on if you can use the data from the test. Your users do not need to have a complete idea of what they are signing up for; hence, you must inform them.

2. Bring in a broader demographic

Ensure the people you get to participate in your usability test have different perspectives on your product. That means you need to include people from different demographics, market segments, usage behaviors, and abilities. When you intentionally bring in a broader demographic of users, you will build better products.

Each demographic will know what is best for their group and can point out specific inconsistencies within your product. Here, the aim is to help you widen your gaze and see things differently.

3. Pilot testing is important

It’s always an excellent option to run a pilot usability test with preferably another team in your organization. Doing this will help you solve issues you may have missed and ensure your product is ready for usability testing with your target audience. 

4. Ensure you set a criteria

It is essential to have set criteria for usability testing. Ask yourself what success means for your product. What are your exact goals, and what qualifies the result as a failure? When you have these outlined, you can run an effective usability test.

5. Consider the length of the test

Consider the time you spend on each usability test. Not everyone can devote an entire day to checking your product for its usability. Therefore, the time you spend on your usability test needs to be enough for you to be confident with the result. If you’re unsure of the time, run a pilot test first to give you a better idea. Remember, it is better for you to run multiple tests than one test that leaves the users exhausted. Asking too much from your users will only give you poor test results.

How does HeadSpin help streamline usability testing for enterprises?

Leveraging deep ML models and robust real-device infrastructure, the HeadSpin Platform enables comprehensive testing of applications on real devices, ensuring an end-to-end assessment of performance. This specialized usability testing Platform provides a holistic view of app performance across various devices and networks, making it a powerful tool for effective user experience testing. Enterprises benefit from HeadSpin’s dedication to enhancing digital user experiences through their cutting-edge Platform, which enables them to optimize their applications for optimal usability and performance.

● Capturing real time performance data 

With HeadSpin, you’re not just observing user behaviors; you’re getting a real-time stream of performance data. This data aids in discerning bottlenecks and ensuring that user interactions remain seamless and responsive, no matter the device or network.

● Actionable insights

HeadSpin’s granular analytics help understand where users face challenges, and you can make focused enhancements to improve their journey. The in-depth ML algorithms help identify the root causes of the high-priority issues and provide insights into resolving them. With actionable AI-based testing insights into user interactions, teams can quickly identify and rectify usability glitches, ensuring a smoother experience for the end-users. 

● Testing on real devices

HeadSpin’s global device infrastructure provides access to thousands of real SIM-enabled devices to test and monitor app performance and user experience. Testing and QA teams can connect to a wide array of real devices across 90+ locations worldwide, ensuring the application is tuned to a broad range of users with varied preferences.

Conclusion

In the digital age we reside in, the user base can dilute in a blink of an eye. Regardless of what scale the enterprise is operating in, focusing on user-centricity through the deployment of an effective usability testing platform is critical, enabling businesses to remove any biases from the equation and obtain data-driven insights, which can be used to improve the user experience of products. An in-depth grasp of usability testing’s facets empowers researchers to navigate the vast expanse of methodologies, ensuring that every digital interaction resonates with the user’s expectations and desires.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/a-complete-guide-to-usability-testing

In today’s landscape, numerous businesses and corporations thrive by relying on diverse mobile applications. Examples include WhatsApp, Snapchat, Facebook, Pokemon Go, and various mobile wallet services, all of which owe their functionality to rigorous testing methodologies.

Certain e-commerce mobile apps have also recognized the significance of crafting a robust testing strategy using mobile application methodologies. These apps have undergone comprehensive testing using a variety of mobile strategies to ensure their compatibility. Mobile apps empower both corporations and enterprises to create scalable applications accessible across multiple platforms.

The process of developing mobile application software entails thorough testing for functionality, usability, and consistency. This testing involves a combination of automation and manual testing techniques. Given the increasing complexity of mobile applications, there is a growing need for comprehensive end-to-end testing. This ensures that mobile apps are not only downloadable but also operate effectively, delivering a consistent experience to users across different devices.

The rising importance of Appium

As the demand for mobile apps is on a continuous surge, both corporations and enterprises are turning to Appium to create scalable apps accessible across multiple platforms. This shift is driven by the need to build mobile applications that not only meet user expectations but also operate effectively and consistently. 

Introducing Appium

Appium is an open-source automation tool widely used for testing mobile apps on Android and iOS platforms. It enables you to automate testing for native, hybrid, and mobile web applications. Appium uses a “write once, run anywhere” approach, meaning you can use the same codebase for testing apps on both iOS and Android devices.

What are the different types of mobile apps?

The three primary types of mobile applications are:

• Native apps: These are built specifically for a particular platform, like Android or iOS, using platform-specific programming languages (Java/Kotlin for Android, Swift/Objective-C for iOS).
• Hybrid apps: These combine elements of both native and web apps. They are often developed using web technologies (HTML, CSS, JavaScript) but are wrapped in a native container to run on mobile devices.
• Web apps: These are mobile-optimized websites that can be accessed through a mobile browser.

Appium can automate testing for all three types of mobile apps.

Appium testing: It’s prerequisites, requirements, and processes

Prerequisites for Appium Testing

Before you start using Appium, you’ll need:

• Java Development Kit (JDK): Install the JDK on your system.
• Android SDK and iOS SDK: Install the Android SDK and iOS SDK and set up virtual devices or connect physical devices for testing.
• Appium server: Download and install the Appium server on your machine.
• Appium client libraries: Choose a programming language (e.g., Java) and install the corresponding Appium client library.
• IDE: Utilize an integrated development environment (IDE) like Eclipse or IntelliJ IDEA for writing and executing Appium tests.

Appium Inspector 

Much like Selenium IDE’s record and playback tool, Appium offers an ‘Inspector’ feature for recording and playback. This functionality allows you to record and replay native application interactions by examining the Document Object Model (DOM) and generating test scripts in your preferred programming language. It’s important to note that, as of now, Appium Inspector lacks support for Microsoft Windows. On Windows, it can successfully launch the Appium Server but is unable to inspect elements. As an alternative, you can utilize the UIAutomator viewer for inspecting elements.

Here are the steps to begin using Appium Inspector on your Mac machine:

Step 1: Start by downloading and launching your Appium server. Make sure the server is configured with the default IP Address (0.0.0.0) and port (4723).

Step 2: Start the simulator on your Mac machine.

Step 3: Next, open Appium inspector and set ‘Desired Capabilities’ and click ‘Start Session’

Step 4: Appium Inspector will display the element hierarchy in a column-wise structure, allowing you to view and interact with the elements. You can also perform actions using buttons like Tap, Swipe, and more.

Step 5: You can start recording by clicking the ‘Start Recording’ button. When you’re done recording or inspecting, simply click on the ‘Stop’ button to conclude the process.

Start Automating Android Apps Using Appium

Following is the basic procedure for Android app testing with Appium —

Step 1: Initialize Appium

import io.appium.java_client.AppiumDriver;

import io.appium.java_client.MobileElement;

import io.appium.java_client.android.AndroidDriver;

import org.openqa.selenium.remote.DesiredCapabilities;

public class AndroidTest {

    public static void main(String[] args) {

        DesiredCapabilities capabilities = new DesiredCapabilities();

        capabilities.setCapability(“platformName”, “Android”);

        capabilities.setCapability(“deviceName”, “Your_Device_Name”);

        // Add other desired capabilities

        AppiumDriver<MobileElement> driver = new AndroidDriver<>(“Appium_Server_URL”, capabilities);

    }

}

Step 2: Write test cases

Write test cases using Appium’s methods to interact with elements in the Android app.

Locating Elements Using UIAutomatorViewer

UIAutomatorViewer is a tool provided by Android SDK to inspect and locate elements in Android apps. Use it to identify element locators for your Appium tests.

1. Launch UIAutomatorViewer from the Android SDK’s “tools” directory.
2. Connect and prepare device: Ensure your Android device or emulator is running and connected with USB debugging enabled.
3. Capture screenshot: Use UIAutomatorViewer to capture a screenshot of the app.
4. Inspect elements: Hover over the screenshot to identify elements. Click on elements to view attributes like resource-id, class, text, etc.
5. Identify element locators: Note down the relevant attributes to serve as element locators in your Appium scripts.
6. Exit UIAutomatorViewer: Close UIAutomatorViewer when you have the necessary element locators.
7. Use locators in Appium scripts: In your Appium test scripts, utilize the element locators to interact with app elements during testing.

Start Automating iOS Apps with Appium

Following is the basic procedure for iOS app testing with Appium—

1. Install Appium and dependencies:

Ensure you have Appium installed on your machine, following the installation process outlined in the previous sections.

2. Prepare iOS environment:

To automate iOS apps, you’ll need a macOS computer. Appium iOS automation requires Xcode, which is only available on macOS.

Additionally, make sure you have the Xcode Command Line Tools installed.

3. iOS simulator or device:

You can automate iOS apps on either an iOS simulator or a physical iOS device. Ensure you have access to the device or simulator you want to use.

4. Start Appium server:

Launch the Appium server, as described earlier, specifying the server address and port in your Appium settings.

5. Define desired capabilities:

In your Appium test script, define the desired capabilities specific to iOS testing. These capabilities include information like the platform name, platform version, device name, and app path.

6. Set automation framework:

Appium uses XCUITest as the automation framework for iOS testing. Set the automationName capability to “XCUITest” to indicate that you’re using this framework.

7. iOS App installation:

Specify the app you want to automate using the app capability. You can provide the app path or the bundle identifier of the app.

8. Initialize Appium driver:

In your test script, create an Appium driver instance for iOS by specifying the Appium server’s URL and the desired capabilities you’ve defined.

9. Write test cases:

Develop your test cases using Appium’s methods and functions to interact with iOS app elements. You can find elements using locators like XPaths or accessibility IDs.

10. Execute test cases:

Run your Appium test script to execute the automation. The script will launch the iOS app on the chosen device or simulator and perform the specified actions.

11. Analyze test results:

Appium provides detailed test logs and reports to help you analyze test results. You can identify any issues or failures in your test cases.

12. Debug and refine:

If any test cases fail or need improvement, debug and refine your scripts accordingly. This iterative process ensures the reliability and effectiveness of your automated tests.

Limitations

• No support for Windows apps: Appium primarily supports Android and iOS, so testing Windows apps is not possible.
• Limited iOS simulator features: Some advanced iOS simulator features may not be accessible via Appium.
• Native app limitations: Testing certain native app features, like biometric authentication, can be challenging.

First program for automating an app on Appium

For a complete example, let’s automate a simple scenario – launching an app and verifying the app’s title:

import io.appium.java_client.AppiumDriver;

import io.appium.java_client.MobileElement;

import io.appium.java_client.android.AndroidDriver;

import org.openqa.selenium.remote.DesiredCapabilities;

public class AppiumDemo {

    public static void main(String[] args) {

        DesiredCapabilities capabilities = new DesiredCapabilities();

        capabilities.setCapability(“platformName”, “Android”);

        capabilities.setCapability(“deviceName”, “Your_Device_Name”);

        capabilities.setCapability(“appPackage”, “com.example.app”);

        capabilities.setCapability(“appActivity”, “.MainActivity”);

        AppiumDriver<MobileElement> driver = new AndroidDriver<>(“Appium_Server_URL”, capabilities);

        // Automate your test steps here

        // e.g., driver.findElementByXPath(“//element_locator”).click();

        String appTitle = driver.findElementByXPath(“//element_locator”).getText();

        System.out.println(“App Title: ” + appTitle);

        driver.quit();

    }

}

Replace “com.example.app” and “Your_Device_Name” with your app’s package name and your device’s name.

Appium test for launching the eBay app

Common issues encountered and suggested troubleshooting tips

Error	Troubleshooting tip
Appium server not starting	Ensure you have Appium and its dependencies correctly installed.Check if there are any conflicting processes using the same port (usually 4723). Kill them if necessary.Verify that your system meets the hardware and software requirements for running Appium.
Appium can’t connect to the mobile device or emulator.	Confirm that the device or emulator is correctly connected to the computer via USB or configured for network connection.Restart ADB server (Android Debug Bridge) if working with Android devices.Ensure that USB debugging is enabled on Android devices.For iOS, ensure the device is properly recognized in Xcode and enabled for development.
Appium cannot locate a specific element in the app.	Double-check the element locator (XPath, ID, etc.) used in your test script.Verify that the element is visible and accessible at the time of the test.Add waits or implicit/explicit waits to ensure the element loads before interacting with it.
The element that was located is no longer part of the DOM (Document Object Model).	Implement error-handling mechanisms like try-catch blocks to handle this exception.Re-locate the element or refresh the page before interacting with it.
The app crashes or becomes unresponsive during test execution.	Check for memory issues or memory leaks in the app’s codeEnsure your test script doesn’t overload the app with rapid interactions.Increase the device or emulator’s available resources if possible.
Tests produce inconsistent results on different devices or emulators.	Standardize test environments by using the same app version, device configuration, and operating systemImplement synchronization points in your tests to ensure a consistent state before interacting with elements.

‍

Conclusion: Using Appium with HeadSpin

Using Appium with HeadSpin for testing mobile apps provides you with a powerful combination of automation capabilities and a comprehensive mobile device testing platform. HeadSpin offers a cloud-based infrastructure that allows you to test your mobile apps on real devices and networks from various locations worldwide. 

If your team already has existing Appium tests, you can seamlessly integrate them with the HeadSpin Platform and execute them on any accessible device. Appium takes care of the functional aspects of the test, while HeadSpin’s data and insights enhance the non-functional aspects.

In the latest platform version, you can now debug and develop Appium scripts directly in the UI without the need for a separate app or desktop client. Additionally, apart from the standard Appium capabilities and W3C WebDriver specifications, the Appium server on each HeadSpin host supports various custom capabilities. The HeadSpin Appium load balancer, running in the cloud, offers additional features for device selection and redundancy control.

Appium Inspector, a valuable component of Appium’s feature set, is typically used to identify UI elements when creating Appium automation scripts. It typically works in conjunction with an actual device from your local machine or a simulator (for iOS) or emulator (for Android) to explore the app’s view hierarchy.

However, HeadSpin’s Device Remote Control UI streamlines the setup process by eliminating the need to prepare simulators/emulators, download apps for testing, and perform various other setup steps. This feature is known as HeadSpin Appium Inspector Integration within the HeadSpin Device Remote Control UI.

HeadSpin’s impact

• Testing with open-source Appium, avoiding vendor lock-in.
• Accelerating test cycles by up to 30% with the HeadSpin Platform.
• Capturing performance data to provide actionable insights.
• Ensuring reliable testing on both iOS and Android devices.
• Guaranteeing compatibility with two-way audio and biometric testing.

These innovations are designed to enhance your mobile testing and performance monitoring efficiency, making your testing processes more effective and robust.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/android-and-ios-app-testing-with-appium

Today’s connected car systems are a storehouse of data. These cars are not only improving consumer experiences but also offering revenue and cost benefits to mobility companies, including OEMs, car dealers, suppliers, tech players, and much beyond. The automotive industry is increasingly open to harnessing data from connected cars. To assist mobility companies seeking opportunities in this domain, surveys have come up with an evaluation of the potential market value of data-driven services. HeadSpin’s analysis explores promising use cases and identifies strategic approaches to enhance their prospects for success.

Monetization opportunity for OEMs and other mobility sectors

Consumers highly value connectivity, as evidenced by one of McKinsey’s surveys, where 37 percent expressed a willingness to switch car brands for connectivity improvements. In some regions, this figure was even higher, such as 56 percent in China. Moreover, 39 percent of consumers showed interest in unlocking additional digital features post-purchase, rising to 47 percent among premium OEM customers. Failure to meet these connectivity expectations can lead to customer attrition.

Numerous OEMs have grappled with challenges related to connectivity and software development, resulting in unfavorable customer feedback and project delays. Only a select few have managed to excel in the realm of software-defined vehicles and effectively capitalize on vehicle data monetization. These successful companies concentrate on three pivotal activities:

• Extensive data access: Enabling continuous improvements by granting access to 1 to 2 terabytes of daily vehicle data.
• Monetization: Emphasizing monetization through subscriptions and paid OTA upgrades.
• Swift integration: Achieving rapid idea-to-vehicle integration in about six weeks, a strategy that has benefited EV-focused OEMs.

Connected car performance analysis is also presenting clear monetization opportunities today. Connected vehicles generate valuable data streams. OEMs can explore monetization opportunities by providing data-driven services to third parties, such as insurance companies, advertisers, and smart city initiatives. This diversification of revenue streams can bolster OEM profitability.

However, most companies are much less successful in optimally monetizing the situation, and the primary reasons include:

1. Failing to capture customer interest and distinguish their services

To engage customers effectively, OEMs must differentiate their services from the wealth of connectivity offerings available on smartphones. Complex onboarding processes, service execution issues, and communication challenges hinder consumer adoption. In the B2B realm, many customers remain unaware of car data’s potential benefits, resulting in underutilization.

2. Neglecting organizational transformation

Companies need to restructure and develop dedicated cross-functional units to monetize data effectively across the vehicle’s lifecycle. Siloed functions, talent gaps, and outdated business models hamper progress.

3. Overlooking ecosystem development for scalability

Automakers should collaborate with existing service providers for rapid scaling in areas like automated charging, remote monitoring, and targeted advertising. Isolated solutions hinder core competency focus and limited partnerships in the B2B space limit market reach. This landscape is evolving, though.

How have connected cars reformed the current automobile landscape?

• Enhanced safety: Advanced driver assistance systems (ADAS) use real-time data to provide features like lane-keeping assistance, adaptive cruise control, and collision avoidance, significantly improving driver safety.
• Convenience: Features such as remote engine start, climate control, and vehicle diagnostics can be accessed through mobile apps, offering drivers unparalleled convenience.
• Efficiency: Real-time traffic data helps optimize route planning, reducing travel time and fuel consumption.
• Entertainment: Infotainment systems provide passengers with internet access, streaming services, and more, making journeys more enjoyable.

However, to completely garner the connected car systems, it is critical to test and validate these systems for optimized performance and user experiences. As a means to achieve this, automobile companies are focusing on leveraging real-time connected car analytics end-to-end.

Importance of real-time connected car testing and analytics in automobile testing

The automotive industry, in several scenarios, is shifting towards digitization, connectivity, and data-driven decision-making, making real-time analytics an indispensable tool for manufacturers, service providers, and consumers alike. 

The primary factors making real-time analytics a key are:

● Improved testing precision

Real-time connected car analytics provide testers with a continuous stream of data from various vehicle sensors and systems. This influx of real-time data allows for more precise and dynamic testing scenarios. Testers can monitor critical parameters such as engine performance, braking, suspension, and emissions in real-time, enabling them to detect and address issues promptly.

● Simulated real-world testing

Modern vehicles are equipped with several sensors and communication modules that capture data during real-world driving conditions. Real-time analytics enable testers to replicate conditions like these in a controlled environment, such as a test track or simulation facility. This simulation of real-world scenarios helps assess a vehicle’s behavior under different driving conditions, from urban traffic to highway speeds.

● Early issue detection

Real-time analytics provide instant feedback on a vehicle’s performance, allowing testers to identify and address issues as they occur. This proactive approach to issue detection is invaluable in preventing potentially costly recalls and safety hazards. It ensures that vehicles meet regulatory standards and consumer expectations.

● Safety validation 

Safety is a paramount concern in the automotive industry. Real-time connected car analytics enable safety validation tests to be conducted more comprehensively. Testers can monitor critical safety systems such as adaptive cruise control, collision avoidance, and lane-keeping assistance in real-time. This validation process helps ensure that these systems function correctly and reliably, reducing the risk of accidents.

● Efficient data analysis for performance

Real-time analytics streamline the process of data analysis during testing. Instead of manually collecting and processing data after each test, testers can rely on automated systems that provide immediate insights. This efficiency accelerates the testing cycle, allowing manufacturers to bring vehicles to market faster while enhancing connected car-performance analysis.

In the event of a vehicle recall, connected car performance analysis helps OEMs pinpoint affected vehicles with precision. This targeted approach ensures that only vehicles at risk are recalled, minimizing disruption for unaffected customers.

The common challenges faced by OEMs while leveraging real-time analytics in testing

Following are the major challenges faced while testing the connected car applications.

Consumer demands around mobile-at-par experiences: Very often, consumers expect infotainment systems to deliver experiences equivalent to mobile experiences in terms of responsiveness, loading time, and features like communication, music, and other entertainment features. This makes testing complex in terms of catering to multiple requirements of users.

Complex and time-consuming testing and optimization processes: Testing connected car applications involves complex scenarios, such as real-time data streaming and integration with various vehicle systems. This complexity often leads to lengthy testing and optimization processes, affecting time-to-market.

Stringent requirements for addressing potential safety concerns: As connected cars keep evolving in terms of comfort and safety across networked vehicle components and systems, the testing requirements become more and more complex. The introduction of new features and information in connected cars can lead to driver distractions. Ensuring that the user interface and interactions do not compromise safety is a critical challenge. Additionally, with cars becoming more connected with external sources, for instance, vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-cloud, the connected cars require more efficient and extensive testing to ensure the reliable and safe operation of these complex systems.

Resolving connectivity issues affecting user experience: Connectivity issues can disrupt the user experience. This includes challenges related to maintaining a stable connection to the internet, ensuring consistent data transfer, and handling network transitions seamlessly.

Testing in geo-distributed teams: The connected cars require multiple sub-components from different sources, such as the cameras, proximity sensors, user touch, display units, and much more. Different components need to be tested and monitored by different teams in different locations, which makes it difficult to seamlessly manage testing labs across multiple geographies. This often leads to resource limitations and inefficient testing, thereby compromising the results.

The aspects to be tested in connected cars

• In-vehicle infotainment testing: In-vehicle infotainment systems are a central component of connected cars, providing entertainment, navigation, and connectivity features to users. Testing should focus on verifying the functionality of multimedia interfaces, touchscreen responsiveness, audio quality, voice recognition, and compatibility with external devices such as smartphones and tablets. It also includes testing the integration of software applications, ensuring they work seamlessly within the infotainment ecosystem. The goal is to deliver a rich and intuitive infotainment experience to drivers and passengers.
• Connected user experiences: Connected cars rely on various sensors, connectivity protocols, and data exchange mechanisms to offer a range of user experiences. Testing in this area encompasses evaluating how well these systems perform in real-world scenarios. It involves testing the reliability of connectivity, including Bluetooth, Wi-Fi, and cellular networks, to ensure uninterrupted access to services and data. Additionally, user experiences related to remote vehicle control, over-the-air (OTA) updates, and vehicle-to-infrastructure (V2I) communication must be thoroughly tested to guarantee a smooth and secure interaction between the car and its ecosystem.
• Cybersecurity and data privacy: As connected cars become increasingly reliant on data exchange, ensuring the security and privacy of both vehicle and user data is paramount. Testing should focus on identifying vulnerabilities and weaknesses in the car’s network architecture, data transmission, and storage mechanisms. This includes penetration testing to assess the car’s resistance to cyberattacks. Additionally, data privacy testing evaluates whether user data, including personal information and driving habits, is appropriately protected and compliant with relevant regulations such as GDPR. A robust cybersecurity strategy is essential to safeguarding both the vehicle and the user against potential threats.

Improving automobile testing with HeadSpin

HeadSpin’s data science capabilities for automobile testing eliminate the complexities of testing connected vehicle applications, leveraging advanced analytics to evaluate vehicle performance, reliability, and user experience. 

HeadSpin’s solution for automobile testing allows OEMs to enhance development quality and streamline QA processes. This boosts the stability of each release cycle while minimizing rework and post-release investigations. The solution helps:

• Deploy real mobile devices to facilitate testing automation and support developers in their work.
• Measure pertinent performance metrics, ensuring comprehensive evaluation.
• Pinpoint functional and performance issues across 15 critical user scenarios.
• Access source code for all scripts is provided to the automotive OEM team, fostering transparency and collaboration.
• Integrate into the automotive OEM’s CI/CD workflow, enabling post-build automated tests to identify performance regressions. 
• Integrate with Grafana and alerting systems to enhance monitoring and response capabilities.

Deploying HeadSpin

The primary elements constructing the testing solution include HeadSpin’s PBox, AV Box, and the System App/SDK.

• The PBox supports remote manual and automated testing of the devices debuggable over USB
• The AV Box serves as the sound/light isolated enclosure for apps that require audio video testing, like the voice assistants 
• Consumer system app/SDK helps precisely monitor the performance and user experience of Android-based IVI systems

The HeadSpin solution can be seamlessly deployed on the premises of the customer, shared cloud, client-dedicated cloud, or reverse bridge technique (unique to HeadSpin).

• The on-prem deployment involves deploying the solution into an isolated network managed by the customer where no traffic leaves this network. 
• The client-dedicated cloud deployment option, however, creates significant traction as it provides dedicated devices for customers in the HeadSpin cloud and helps isolate customer services, configuration, data, devices, and hosts to a private subnet.
• HeadSpin’s reverse bridge technique, or ‘Create your own Lab,’ simplifies the testing journeys for companies immensely by eliminating their limitations of accessing multiple test labs for different purposes across various locations. CYOL enables distributed testing from anywhere in the world with reduced latency access to remote devices owned by the customer.

How does the HeadSpin automobile solution work?

HeadSpin’s approach to analyzing performance of real-time connected vehicle apps revolves around capturing essential data from devices strategically positioned in the client’s user environments, all seamlessly connected to local networks or Wi-Fi. The diverse deployment options of the HeadSpin appliance ensure secure integration, allowing it to seamlessly mesh with a variety of automated frameworks such as Appium, Selenium, or third-party testing tools. Through remote control UI/debug bridge access, it enables comprehensive device management.

HeadSpin’s deep ML-driven Platform harnesses this data to:

In addition to these capabilities, HeadSpin’s insightful visualizations and analytics dashboards are equipped with customizable KPI tracking. These features make it possible to gain a comprehensive understanding of system performance.

HeadSpin’s automotive solutions are versatile, catering to three primary use cases: Infotainment System Testing, HeadSpin SDK for Android Automotive OS, and connected vehicle apps testing. This adaptability empowers automotive companies to analyze  connected vehicle apps to optimize their performance and ensure unparalleled user experiences.

HeadSpin’s robust approach to data capture, analysis, and performance optimization allows for effective monitoring of real-time connected vehicle apps and facilitates comprehensive performance analysis, enabling it to proactively address issues, enhance user experiences, and drive innovation in the rapidly evolving automotive landscape.

What does HeadSpin offer?

In today’s automotive landscape, consumers expect in-vehicle infotainment experiences that rival the seamless interactions they enjoy on their smartphones. 

To meet these expectations, HeadSpin offers innovative in-vehicle infotainment testing solutions that leverage data science and real-time feedback to drive performance improvements.

Key focus areas:

• User experience enhancement: HeadSpin’s in-vehicle infotainment testing focuses on improving the overall user experience within the vehicle cabin, ensuring that drivers and passengers have access to cutting-edge entertainment and information systems.
• Driver distraction mitigation: In connected vehicles, IVI units are critical in ensuring seamless performance of not only the car but also the driver’s concentration. In case of a glitch in the IVIS in terms of navigation, or music players, and many more can easily jeopardize safety by distracting the driver. HeadSpin’s testing solutions help identify and address potential driver distractions, ensuring a safer driving environment.
• Quality of service: HeadSpin delivers the highest quality service by constantly monitoring and optimizing the performance of in-vehicle infotainment systems.

What does HeadSpin offer for enhancing automotive digital experience?

1. Connected vehicle application testing

HeadSpin’s deep ML models enable OEMs to analyze and monitor core KPIs that impact the performance and user experience of the CV apps. The ML driven Platform and the PBox help monitor the real time user experience of key features of the app like car lock and unlock, window up, bluetooth and much more allowing users to leverage the device as a key to controlling the vehicle (or Phone as a Key/PaaK). 

This capability allows:

• Calculating latency for every use case
• Obtaining historical information, including improvements and degradations
• Calculating Bluetooth connection drop and performance matrix
• Obtaining insights into noisy environment effects and performance data

2. In-vehicle infotainment unit testing

HeadSpin allows OEMs to reverse bridge IVI units to test remotely and capture critical KPIs to improve user experiences.  HeadSpin deploys advanced data science capabilities to close the feedback loop for infotainment and in-cabin digital experience measurement, thereby offering seamless driving experiences. 

HeadSpin’s system app seamlessly integrates with the vehicle’s infotainment system, enabling real-time issue capture directly through the HeadSpin UI. This innovative approach empowers HeadSpin to pinpoint critical user journeys, identify UX issues at every step, and provide a range of solutions to address these issues, all with the ultimate goal of achieving optimal performance for the infotainment system.

3. SDK for Android automotive OS

HeadSpin provides SDKs (Software Development Kits) designed for deployment in vehicles equipped with Android Automotive OS. These SDKs facilitate various testing scenarios, including in-lab testing, in-drive testing, and end-to-end user experience monitoring. 

HeadSpin continuously monitors the driver or user interaction with the Head Unit and Infotainment system. Testers gain access to real-time user data, enabling constant feedback and improvement efforts. This monitoring process captures key performance indicators (KPIs) such as blank screens, network errors, distracting screens, poor connectivity, loading animations, and instances of rage tapping.

The value HeadSpin adds to businesses

HeadSpin solutions empower auto companies to:

• Improve user experiences through proactive repairs and better service, resulting in higher Net Promoter Scores (NPS).
• Increase revenue by delivering superior infotainment experiences attracting and retaining customers who value these features.
• Monetize infotainment unit data, opening up new opportunities for the auto industry. For example, insurance companies can analyze various aspects of car usage to determine accurate premium rates.
• Discover how HeadSpin’s In-Vehicle Infotainment Testing can transform your automotive offerings and enhance user satisfaction.

Bottom line

The automotive connectivity landscape is evolving at an unprecedented pace, offering substantial opportunities for data monetization across the entire ecosystem. Key beneficiaries include data suppliers like OEMs and vehicle fleets, as well as insurance companies, automotive aftermarket businesses, municipalities, infrastructure providers, and various data consumers. It’s essential for all stakeholders to take swift action to harness these opportunities effectively.

HeadSpin’s solutions are the right match for streamlining automobile testing with the unique advantages of executing tests across any location and testing in-car experiences as well as car app experiences for all users.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/real-time-connected-car-performance-analysis

Amidst the dynamic landscape of software and technology, it’s imperative to guarantee that software applications meet user expectations and maintain optimal performance. QA teams are instrumental in this endeavor, utilizing continuous performance testing to promptly identify and tackle performance issues. However, a prevalent hurdle for companies is the lag in testing speed, often falling short of meeting the swift delivery requirements. 

This article is crafted for developers, testers, and project managers, offering comprehensive insights into continuous performance testing and its pivotal significance in securing the success of software applications.

Defining Continuous Performance Testing

Continuous performance testing involves assessing an application’s performance as it faces increased load. Unlike manual performance tests, which can be time-consuming and costly, this approach integrates performance evaluation with every code commit. The aim is to ensure comprehensive test coverage, incorporating new test cases and updating existing ones to reflect evolving application dynamics.

Exploring Various Performance Testing Types

One prominent type of performance testing is load testing, which is widely employed in the field. This test method is designed to replicate multiple users accessing the software concurrently. Its objective is to examine the system’s threshold in processing requests without compromising stability. Load tests are typically automated using scripting languages to enhance execution control and accuracy.

Distinguishing Performance Testing from Continuous Performance Testing

‍● Integrating Performance Evaluation Throughout Development

Continuous testing represents an evolution of traditional performance testing methods. Unlike the conventional approach, often employed post-development (refer to Figure 1), continuous performance testing is seamlessly integrated into the software development life cycle. It involves a persistent evaluation process that runs throughout the development stages.

 ● Essential Contrast: One-Time Testing vs. Ongoing Evaluation

A fundamental distinction between performance testing and continuous performance testing lies in their timing and nature. Performance testing is typically a one-time endeavor, often occurring after the development cycle concludes or just before the product’s release to production. In contrast, continuous performance testing is a perpetual process seamlessly integrated into the software development life cycle.

 ● Traditional Performance Testing vs. Continuous Integration (CI) Approach

Traditional performance tests are commonly executed post-release cycles or at specific milestones rather than being an intrinsic part of the Continuous Integration (CI) process. On the other hand, continuous performance tests are conducted frequently, seamlessly fitting into every build of the development process.

 ● Focus Shift: Version-Centric Load Testing to Application Evolution

Traditional load testing assesses a specific software version’s performance under peak load scenarios. Conversely, continuous load testing ensures that every new application version can sustain peak load conditions efficiently.

 ● Driving Swift Solutions with Continuous Performance Testing

Incorporating continuous performance testing into Continuous Delivery (CD) pipelines is invaluable. When a new application version falls short of the required performance criteria, the entire build is flagged as a failure. This prompts a comprehensive review of the latest code changes, allowing for prompt identification and resolution of performance issues. The essence is to utilize continuous performance testing for early problem detection and swift remediation, averting potential negative impacts on customers or users.

 ● Challenges and Feasibility: Factors Impacting Continuous Load Testing

While continuous load testing holds immense value, some organizations encounter challenges in its implementation. These hurdles may include a need for more production-like environments for various API versions and inadequate test data encompassing traffic permutations. Despite these challenges, the benefits of continuous load testing remain substantial, underscoring its potential for enhancing software performance and reliability.

The Advantages of Continuous Testing

1. Timely Identification of Performance Issues: Consistent performance testing in the development cycle allows for early recognition of potential performance issues, effectively mitigating the risk of encountering more severe problems later.
2. Swift Feedback Loop: Embedding continuous performance testing into the development process offers immediate insights into the application’s performance, enabling developers to detect and address any emerging concerns promptly.
3. Enhanced User Satisfaction: Ensuring optimal application performance, especially during high usage, significantly enhances user experience. This, in turn, leads to higher levels of user satisfaction and increased engagement.
4. Cost-Efficient Development: Identifying and rectifying performance issues in the early stages of development helps curb overall costs. It averts the need for costly rework or extensive infrastructure modifications that might otherwise be necessary later in the development life cycle.
5. Optimized Resource Utilization: Automating performance testing optimizes the allocation of time and resources within development teams. This efficiency allows teams to concentrate on various aspects of development beyond performance, boosting overall productivity.
6. Elevated Software Quality: Continuous performance testing thoroughly evaluates an application’s performance aspects. This, in turn, contributes to an overall improvement in the quality and reliability of the application.

Understanding the Beneficiaries of Continuous Performance Testing

Determining the optimal fit for continuous performance testing largely depends on a company’s unique requirements. Different organizations find varying degrees of value in incorporating continuous performance testing, especially those with specific characteristics:

• Companies with a Large User Base: Organizations serving a substantial user base often find continuous performance testing particularly beneficial. The larger the user base, the more critical it becomes to ensure seamless application performance under various conditions.
• High Interaction Volumes or Seasonal Traffic Spikes: Businesses experiencing high and fluctuating interaction levels or seasonal spikes in traffic can significantly benefit from continuous performance testing. It provides insights into how the application handles varying loads, aiding optimization strategies.
• Projects with Significant Time or Financial Investment: Projects with substantial time or financial investments and a long-projected lifespan stand to gain significantly from continuous performance testing. It helps protect the investment by proactively identifying and addressing performance issues.
• Companies with Abundant Staff Resources: Organizations with a large staff pool can effectively implement continuous performance testing due to the available resources and expertise.

Each company’s unique needs dictate the relevance and integration of a continuous performance testing system. For instance, DevOps teams dealing with smaller applications that don’t often experience traffic spikes might not find continuous performance testing as crucial. For such scenarios, periodic end-to-end testing provides valuable insights for architectural decisions, application size optimizations, and handling heavy workloads efficiently.

Critical Challenges in Continuous Performance Testing

1. Test Environment Complexity: Establishing a realistic test environment proves complex, particularly for applications relying on intricate infrastructures like microservices or cloud-based services. The absence of precise performance-related requirements in user stories also exacerbates this challenge.
2. Effective Test Data Management: Ensuring test data accurately mirrors real-world scenarios poses a challenge, especially for large data processing applications. This becomes more pronounced when DevOps teams need more expertise in performance engineering, particularly in organizations where external teams handle test data.
3. Test Script Maintenance: Regular maintenance of test scripts is vital to simulate accurate user behavior and generate realistic loads. This involves setting up automated tests, executing them regularly, and developing comprehensive performance reports using cloud-based performance testing tools with built-in management and reporting capabilities.
4. Seamless Tool Integration: Seamlessly integrating performance testing tools into the development process can prove challenging, particularly for organizations with intricate workflows or legacy systems. A notable obstacle is the need for more accountability within development teams regarding performance testing and the absence of observability tools in the development pipeline.

Initiating Continuous Performance Testing: A Starting Guide

Embarking on continuous performance testing requires a solid foundation with a functional Continuous Integration (CI) pipeline in place. Here are the essential steps to get started:

Step 1: Gather Business-Centric Information: Understand the expected request load to maintain current SLAs and identify critical application functionalities such as login, order processing, or checkout.

Step 2: Create Performance Tests: Test the API layer using tools like Speedscale, BlazeMeter, ReadyAPI from SmartBear, or Apache JMeter. Store these tests in your main repository, treating them as valuable assets and focusing on their quality.

Step 3: Define Test Scenarios: Select and design scenarios covering critical system areas, aligning with realistic user usage patterns. Aim for end-to-end user experience, using specific numbers instead of ambiguous terms like “heavy load.”

Step 4: Analyze Results and Address Issues: Compile comprehensive and understandable reports after running tests. Address identified performance issues by incorporating them into your product backlog for prioritized resolution. These initial test results serve as a baseline for future tests.

Conducting these processes in a test environment resembling the production setup is ideal. Tools like Speedscale, replicating production traffic, are valuable for this purpose. However, some companies opt for production testing due to automation complexity and the high costs of maintaining production-like environments despite accurate data collection and replication challenges.

Integrating Continuous Performance Testing into CI/CD Pipeline: A Step-by-Step Guide

Step 1: Clarify Performance Testing Objectives: Clearly define your performance testing objectives. Determine the metrics you intend to measure, the scenarios to test, and the performance thresholds you aim to establish.

Step 2: Establish Organizational Accountability: Foster a culture of accountability to address potential challenges. Create a formula where Accountability = Awareness + Visibility:

• Awareness: Encourage teams to engage in early discussions about performance aspects.
• Visibility: Ensure that performance factors and acceptance criteria are incorporated into the definition of done.

Step 3: Configure a Performance Testing Environment: Prepare a testing environment mirroring your production setup. Define what performance aspects to test, such as response time, throughput, and scalability, to determine the specific requirements for this testing environment.

Step 4: Define Performance Thresholds: Set performance thresholds aligning with your application’s performance requirements. These thresholds should be based on crucial performance metrics like response times, throughput, and other relevant factors.

Step 5: Thoroughly Analyze Test Results: Post each performance test conduct a detailed analysis of the results to pinpoint any performance-related issues or bottlenecks. Leverage the collected performance metrics to optimize your application’s overall performance.

By following these steps diligently, you can seamlessly integrate continuous performance testing into your CI/CD pipeline, enhancing your development process’s overall efficiency and reliability.

Automated Continuous Performance Testing: Enhancing User Experience

In a company employing Continuous Integration/Continuous Deployment or Delivery (CI/CD), continuous performance testing holds the utmost significance, particularly within the CD process. This entails stress testing the platform and ensuring optimal availability and response times for dashboards.

Performance testing automation becomes pivotal in proactively identifying potential slowdowns before they escalate and detrimentally impact the user experience. Consistent automated performance testing is essential to ensure an exceptional user experience and maintain high levels of user satisfaction. This involves regular evaluation of dashboard performance post-delivery to anticipate and address any emerging slowdowns swiftly.

How HeadSpin Facilitates Seamless Continuous Performance Testing

HeadSpin offers several features and capabilities that enable businesses to integrate continuous performance testing into their development processes seamlessly:

Here are general ways to optimize continuous performance testing with HeadSpin:

1. Performance Monitoring and Analytics: The platform offers real-time performance monitoring and analytics, allowing businesses to track key performance metrics and identify performance bottlenecks. Detailed insights help in proactive optimization and continuously improve the application’s performance.
2. Automated Testing and CI/CD Integration: HeadSpin integrates with widespread continuous integration and continuous deployment (CI/CD) tools, allowing automated performance testing as an integral part of the development workflow. This ensures that performance testing is conducted consistently and efficiently with each code change, facilitating early detection and resolution of performance-related issues.
3. Load Testing and Scalability Assessment: HeadSpin enables load testing to evaluate how the application handles heavy user traffic and concurrent usage. This is critical for assessing scalability and ensuring the application can scale effectively during peak usage.
4. Real-Device Testing: HeadSpin allows you to perform tests on real devices, enabling a realistic evaluation of your mobile app’s performance under real-world conditions. This includes devices, operating systems, and network conditions across 90+ global locations.

Businesses can seamlessly incorporate continuous performance testing into their development lifecycle by leveraging these capabilities. This approach ensures that the application meets performance expectations, enhances user experience, and contributes to the business’s success in the competitive app market.

Bottom Line

Continuous performance testing is the bedrock of exceptional user experiences in software development, requiring ongoing process refinement for competitive advantage. Early performance monitoring, even pre-launch, saves time. It’s a proactive shield against future issues and infrastructure degradation, warranting clear goals and metric-driven progress tracking for sustained improvement and user satisfaction.

HeadSpin offers a comprehensive suite of tools and features that empower development teams to conduct thorough and automated performance testing.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/guide-to-continuous-performance-testing-for-developers

Understanding app performance is crucial for delivering a seamless user experience. HeadSpin session reporting provides a comprehensive analysis of user interactions, crashes, latency, and other essential metrics, aiding in optimizing app experiences. One powerful feature of HeadSpin is the ability to label time series KPIs during a session.

In this tutorial, we will explore how to use HeadSpin labeling capabilities through its Timeseries API to mark regions of interest based on specific KPI thresholds within a session.

The Power of HeadSpin Session Reporting

HeadSpin session reporting offers a detailed view of user interactions within mobile applications, providing invaluable insights into app performance. By capturing and analyzing key metrics, such as Net CPU usage, memory utilization, and battery drain, these reports empower developers and stakeholders to make data-driven decisions to enhance app functionality and user satisfaction.

One unique aspect of HeadSpin’s reporting capabilities is the ability to set thresholds and create labels on device KPIs using the captured time series data. This lets users focus on specific session areas, facilitating a deeper understanding of app behavior under various conditions.

Key Terms: Understanding Sessions, Labels, and Time Series Data

Before delving into the tutorial, let’s familiarize ourselves with some essential terms related to HeadSpin session reports and HeadSpin labeling capabilities:

• Sessions: Sessions refer to reports encompassing highly detailed analytics and Key Performance Indicators (KPIs) captured using a session capture tool seamlessly embedded into our user interface.
• Labels: HeadSpin provides a sophisticated labeling system enabling users to mark specific regions within a session. This feature proves invaluable when focusing on particular areas of interest during analysis.
• Time Series Data: In the context of HeadSpin, time series data signifies the captured device metrics such as Net CPU usage, memory utilization, battery drain, and more. Analyzing these metrics over time provides crucial insights into app performance and behavior.

Now that we have a foundational understanding of these terms, let’s explore how HeadSpin labeling capabilities can be harnessed effectively using the Timeseries API to mark key regions based on specific KPI thresholds within a session.

Prerequisites: Setting Up the Session and KPI Labeling

Before we explore HeadSpin labeling capabilities and use the Timeseries API effectively, setting up the necessary prerequisites is essential. Here’s a step-by-step guide to get started:

‍1. Creating a Session: We must create a session to begin the analysis. This can be achieved through the following methods:

• If performing a manual test session, use the capture button provided in the UI.

● For automated sessions:

• Add the capability “headspin:capture”: “true” in your Appium script.
• Alternatively, use the curl command:

curl -X POST https://TOKEN@api-dev.headspin.io/v0/sessions -d ‘{“session_type”: “capture”,”device_address”: “Device address is available in the UI “}’

2. Accessing the Report: Once the session is created, you will receive a report, which can be accessed in the “Performance Sessions” tab.

3. Accessing the Session for Labeling: Go to the “P” tab and open the specific session you intend to label.

Labeling for Time Series KPIs with HeadSpin

 1. Using the API via the UI: To begin labeling using the Timeseries API within the UI, follow these steps:

• Choose the time-series KPI you want to label for the session. Remember to modify the KPI name to lowercase and replace spaces with underscores (e.g., “Net CPU” becomes “net_cpu”).
• Use the API by selecting the region of interest within the session. You can achieve this by clicking on the starting point and dragging to the desired region.

• Once the region is selected, a pop-up will appear. Choose “Type” as “Analysis: Time Series Request,” and enter the “Category” and “Name.”
• Now, you can add parameters to mark the KPIs. The parameters are defined using a JSON-like format.

2. Generating Labels using the “Range” Method: The “Range” method allows you to mark specific regions of the time-series KPI by setting parameters like upper and lower limits. Here are the parameters to utilize:

‍● List of parameters:

• “lower_limit”: Sets the lower limit for the range.
• “upper_limit”: Sets the upper limit for the range.
• “include_lower_limit”: Set to true to include the lower limit in the range.
• “include_upper_limit”: Set to false to exclude the upper limit in the range.
• “duration_threshold_ms”: If the duration of the identified time interval (that meets the range condition) is less than this threshold, the time interval is discarded.
• “merge_threshold_ms”: After applying the “duration_threshold_ms”, this threshold is applied. If the gaps between any consecutive time intervals are equal to or less than this threshold, the consecutive time intervals are merged into one.

● Example: 

If you wish to mark regions with Net CPU between 30 – 41% values, you can set the lower limit to 30 and the upper limit to 41. The code to be entered in the data field will look like this:

{

    “method”: “range”,

    “time_series_key”: “net_cpu”,

    “parameters”: {

        “upper_limit”: 41,

         “lower_limit”:30,

        “duration_threshold_ms”: 100

    }

}

Here, in the range selected, the API marks regions where the net CPU usage is above 30% and below 41%, and any region that has this value for less than 100ms will be discarded. The result will be as shown in the screenshot below:

The above picture shows the Net CPU time-series KPI on the top, and the labels are created at the bottom.

For more information and advanced analysis options, refer to the following: https://www.headspin.io/docs/session-analysis-api#time-series-data-analysis

Wrapping Up

HeadSpin’s session reporting and labeling capabilities empower users to gain deep insights into app performance by analyzing time series KPIs. By leveraging the Timeseries API and techniques like the “Range” method, developers and stakeholders can efficiently mark regions of interest based on specific KPI thresholds. This enhances their ability to optimize app functionality and deliver exceptional user experiences.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/headspins-time-series-kpis-labeling

Do you know Netflix has more than 220 million paid memberships? Today, this OTT application is one of the most popular, with superior streaming quality, usability, and flawless user experience for its subscribers globally. Now, whether you’re streaming your favorite show on Netflix, catching up on the latest news via a news app, or playing video games on your gaming console, a seamless and enjoyable experience is non-negotiable. This is where OTT (Over-The-Top) services come into play. With the proliferation of OTT platforms and devices, ensuring top-notch user experiences has become more critical than ever. This drives enterprises to focus on OTT automation testing as the key to optimizing the performances of the OTT devices and apps, thereby improving streaming quality and user satisfaction.

User experience: The key to the success of an OTT platform

● Viewer expectations soar to new heights

In today’s digital landscape, viewers have become connoisseurs of content. They expect more than just access to shows; they demand an immersive, user-friendly, and tailored experience. Any glitches, buffering, or navigation issues can quickly turn them away.

● Loyalty equals longevity

In the OTT realm, loyalty is akin to a currency. Subscribers who enjoy a seamless experience are more likely to stick around, renew subscriptions, and even recommend your service to others. On the flip side, viewers encountering problems are prone to churn.

● Revenue streams depend on UX

For OTT platforms, revenue flows from subscriptions and advertisements. A flawless UX keeps subscribers engaged and more likely to interact with ads, thus boosting revenue. Conversely, a poor UX can lead to financial losses as subscribers leave and ad interactions dwindle.

● Protecting your brand reputation

In the digital age, one bad experience can tarnish your brand’s reputation. Negative word-of-mouth spreads quickly through social media and reviews. Upholding a positive reputation is crucial to attracting and retaining viewers.

● Staying competitive in the ux race

In this fiercely competitive landscape, OTT providers must continually enhance their UX to stay ahead. Innovations in design, personalization, and content delivery are essential for staying in the game.

What are some of the most popular devices for OTT streaming?

When it comes to over-the-top (OTT) streaming, several popular devices have become household names, each offering unique features and content libraries to cater to diverse preferences. Here are some of the most sought-after OTT streaming devices worldwide:

A. Roku Streaming Stick:

• Known for its user-friendly interface and a vast selection of apps, Roku is a top choice for many.
• Offers a wide array of streaming channels, including popular ones like Netflix, Hulu, and Disney+.

B. Amazon Fire TV:

• Amazon’s Fire TV Stick provides access to a multitude of apps, including Amazon Prime Video.
• Its voice-activated remote control makes navigation convenient.

C. Apple TV:

• Apple TV is synonymous with quality and seamlessly integrates with the Apple ecosystem.
• Features a rich library of content from Apple TV+ and other popular streaming services.

D. Google Chromecast:

• Chromecast is a budget-friendly option that allows you to cast OTT content from your mobile device to your TV.
• It’s perfect for users who prefer to control their content from their smartphones.

E. Xbox and PlayStation Consoles:

• Gaming consoles like Xbox and PlayStation have evolved into powerful media centers.
• They offer a wide range of streaming apps and are favored by gamers and media consumers alike.

F. Smart TVs:

• Many modern Smart TVs come with built-in streaming capabilities, eliminating the need for additional devices.
• They often feature dedicated app stores for downloading your favorite streaming apps.

G. Google TV (formerly Android TV):

• Google TV brings a smart interface to your television, offering personalized content recommendations.
• Supports various apps, including YouTube, Netflix, and more.

How to perform OTT automation testing on Android TV?

First and foremost, you need to have the required tools and libraries installed. You’ll require:

# Install ADB (Android Debug Bridge) tools

sudo apt-get install adb

# Install Android TV emulator

# You can use Android Studio’s AVD Manager to create a virtual Android TV device.

Following this, create a Python script (e.g., ott_automation.py) and initialize Appium:

from appium import webdriver

# Desired capabilities for your Android TV device

desired_caps = {

    ‘platformName’: ‘Android’,

    ‘deviceName’: ‘Your_Device_Name’,

    ‘appPackage’: ‘com.your.ott.app’,

    ‘appActivity’: ‘com.your.ott.app.MainActivity’,

    ‘automationName’: ‘UiAutomator2’

}

# Appium server connection

driver = webdriver.Remote(‘http://localhost:4723/wd/hub’, desired_caps)

This will help you to interact with UI elements on your Android TV app. Here’s an example of selecting an item from a menu:

# Find the menu element by its accessibility ID

menu_button = driver.find_element_by_accessibility_id(‘menu_button’)

menu_button.click()

# Find and click on a specific item in the menu

item = driver.find_element_by_id(‘menu_item_id’)

item.click()

Write test cases based on your app’s functionalities. Here’s a simple example of checking if a video plays successfully:

# Find and click on a video

video = driver.find_element_by_id(‘video_id’)

video.click()

# Check if the video is playing

is_playing = driver.find_element_by_id(‘play_button_id’).is_displayed()

assert is_playing, “Video is not playing.”

How to perform OTT automation testing on Apple TV?

Performing OTT automation testing on Apple TV involves using specialized tools and frameworks to interact with the device and its applications. 

It is critical to ensure you have the following prerequisites in place:

An Apple TV device for testing.

• A Mac computer with Xcode installed.
• Appium installed on your Mac.
• An OTT application IPA file for Apple TV.

Having fulfilled the requirements, your team needs to create a new Appium project directory and navigate to it in your terminal and initialize a new Appium project using the following command:

Initialize a new Appium project using the following command:

<bash>

appium init

Follow the prompts to configure your project. Make sure to select the correct programming language (e.g., Java, Python, etc.) and testing framework.

In your chosen programming language, create a test script that automates actions on your Apple TV app. Here’s an example using Python with Appium’s Python Client:

from appium import webdriver

# Desired capabilities for your Apple TV device

desired_caps = {

    ‘platformName’: ‘iOS’,

    ‘platformVersion’: ‘your_ios_version’,

    ‘deviceName’: ‘Apple TV’,

    ‘app’: ‘/path/to/your/app.ipa’,

    ‘automationName’: ‘XCUITest’

}

# Initialize the Appium driver

driver = webdriver.Remote(‘http://localhost:4723/wd/hub’, desired_caps)

# Your test actions go here

element = driver.find_element_by_name(‘YourElementName’)

element.click()

# Close the driver

driver.quit()

Replace ‘your_ios_version’ with the iOS version running on your Apple TV and ‘’/path/to/your/app.ipa’ with the actual path to your OTT application IPA file

Run your test script. Appium will launch your OTT app on the Apple TV simulator or the physical device if connected, perform the specified actions, and report the results.

Performing OTT automation testing for Roku TV

Before we dive into testing, make sure you have the following prerequisites in place:

• A Roku TV device.
• A computer with Python installed.

The Roku WebDriver Python library, which you can install using pip:

• pip install roku-wireless

Connect to Your Roku TV

First, you need to establish a connection between your computer and your Roku TV. You’ll need the IP address of your Roku device for this.

from roku import Roku

# Replace ‘YOUR_ROKU_IP’ with the actual IP address of your Roku TV.

roku = Roku(‘YOUR_ROKU_IP’)

Now, you can use the Roku WebDriver library to launch your OTT app on Roku TV.

app = roku[‘YOUR_APP_ID’]

app.launch()

# Replace ‘YOUR_APP_ID’ with the ID of your OTT app on Roku.

You can simulate user interactions with your app to perform various tests. For example, you can navigate through menus, select content, and play videos.

# Simulate pressing the Home button on the remote.

roku.home()

# Simulate pressing the Up button on the remote.

roku.up()

# Simulate pressing the OK button on the remote to select an item.

roku.select()

# Simulate pressing the Home button on the remote.

roku.home()

# Simulate pressing the Up button on the remote.

roku.up()

# Simulate pressing the OK button on the remote to select an item.

roku.select()

You can capture screenshots of your app’s interface to verify its appearance during testing.

# Capture a screenshot and save it to a file.

screenshot = roku.screenshot()

with open(‘screenshot.png’, ‘wb’) as f:

    f.write(screenshot)

You can use assertions to verify that your app is behaving as expected. For example, you can check if a specific element is visible or if a video is playing.

# Check if a video is playing.

is_video_playing = app.is_video_playing()

assert is_video_playing, “Video is not playing.”

# Check if a video is playing.

is_video_playing = app.is_video_playing()

assert is_video_playing, “Video is not playing.”

Close the App

After testing, make sure to close the app gracefully.

# Close the app.

app.close()

How HeadSpin’s AV Platform and data science capabilities help companies

HeadSpin’s data science capabilities 

HeadSpin brings cutting-edge data science capabilities to the table. By leveraging advanced machine learning algorithms, HeadSpin empowers OTT providers to monitor and analyze their applications comprehensively. This translates into a proactive approach to identifying and rectifying performance bottlenecks and other potential issues that could impact user experience.

The AV Platform

One of the standout features of HeadSpin’s platform is its Audio-Visual (AV) capabilities. It allows OTT service providers to evaluate the quality of video and audio streaming, a critical aspect of user satisfaction. By assessing AV performance, businesses can ensure that their content is delivered with impeccable quality, enhancing user engagement and loyalty. 

Here’s how it makes your test automation journey for OTT and media testing simpler:

● Testing across devices:

With the AV Platform, you can seamlessly run tests on a wide range of OTT media devices. It ensures that your content is delivered flawlessly to users, regardless of the device they prefer.

● Robust security:

Securing your content is crucial. The AV Platform enables you to test DRM-protected content, ensuring that your valuable media remains safeguarded while offering a seamless viewing experience.

● Audio testing:

Voice activation and speaker-based applications have become integral in today’s digital landscape. HeadSpin’s AV Platform lets you test these use cases thoroughly, ensuring that audio interactions are smooth and glitch-free.

● Seamless remote control:

The platform simplifies the testing process by allowing you to execute commands on the setup box through the HeadSpin UI. This means you can capture Quality of Experience (QoE) and streaming performance Key Performance Indicators (KPIs) with ease.

● AI-driven insights:

Obtain in-depth insights into perceptual video quality KPIs and AI-based issue cards. Understand the nuances of video and audio performance and make data-driven decisions for optimization.

● Easy playback and analysis:

After running your media tests, easily play them back and analyze them using our advanced video and audio analysis capabilities. This ensures that you have complete control over the quality of your content.

Bottom line

We’ve witnessed Netflix’s rise to the top, and it’s evident that prioritizing user experience alongside great content is key. Ensuring a flawless user experience is where OTT testing shines. We trust you now grasp the significance of OTT testing and understand its execution, especially in the realm of OTT Automation Testing. As a leading OTT testing company, we’re committed to delivering valuable insights.

Article Source:

This article was originally published on:

https://www.headspin.io/blog/mastering-ott-automation-testing-on-different-devices