Discussion on The Vision-First Test Automation Keynote Session with Jason Huggins at #TestMuConf2024! 🚀

[anjuyadav.1398]

In this session, I learned about several tools that are really effective for automotive camera and sensor testing:

• NI LabVIEW: Great for creating and managing test systems for automotive cameras.
• RoboTest: Handy for evaluating automated driving systems and their sensors.
• Vector CANoe: Ideal for testing network and ECU functions, including sensors.
• dSPACE: Useful for hardware-in-the-loop (HIL) simulation, which is crucial for thorough testing.
• MATLAB/Simulink: Excellent for modeling and simulating sensor systems.

From my experience, these tools are essential for making sure that automotive cameras and sensors work as expected in real-world scenarios.

[sakshikuchroo]

I found that choosing the right AI models for automated testing depends on your specific needs. Here are some recommendations based on my experience:

Open Source Models: TensorFlow and PyTorch are great choices. They offer flexibility and a wide range of pre-trained models that can be adapted for different testing scenarios.

1st Party Models: For integration with specific platforms or tools, you might use models provided by companies like NVIDIA for computer vision tasks or Google Cloud AI for various AI services.

3rd Party Models: IBM Watson and Microsoft Azure AI offer robust solutions with built-in features for test automation, including anomaly detection and predictive analytics.

Using these models can enhance your automated testing by improving accuracy and efficiency in identifying issues and patterns.

Hope this answered your query,

[MiroslavRalevic]

During the session, I shared some insights on the challenges and limitations of vision-first test automation based on my own experiences. One big issue is dealing with UI variations like screen resolutions and dynamic content. I’ve tackled this by using adaptive image recognition and visual validation libraries that handle different resolutions.

Another challenge is performance impact. I’ve addressed this by optimizing image capture and processing and using cloud-based services to distribute the load. Overall, these strategies have helped in managing the complexities of visual testing effectively.

Hope my insights where helpful

[Jasminepuno]

During a session I attended, I shared a specific case where vision-first test automation made a notable difference. In one project, our team used vision-first automation to test a complex web application with frequent UI updates.

By leveraging visual testing, we were able to catch inconsistencies and visual bugs that traditional methods missed. This approach significantly reduced the time spent on manual reviews and improved test coverage. The visual tests quickly identified issues like misaligned elements and unexpected layout changes, which were crucial for maintaining a consistent user experience.

In summary, vision-first automation not only streamlined our testing process but also enhanced the accuracy of detecting UI-related issues.

Hope my answer helped resolve you query

[mehta_tvara]

I would like to address this query as I attended this session, and in my experience, Appium and vision-first test automation serve different purposes. While Appium is excellent for automating interactions with web elements in mobile applications, it focuses more on functional testing, like clicking buttons or filling out forms.

In my experience, vision-first test automation complements this by validating the actual visual appearance of the UI. For example, while Appium can confirm that a button exists and is clickable, vision-first testing helps ensure that the button looks correct and is positioned accurately, catching issues that might not be visible through functional tests alone.

Combining both methods provides a more thorough approach to testing, addressing both functionality and visual accuracy.

Hope this helped

[keerti_gautam]

I would like to answer on behalf of the speaker, as I attended his session, and in my opinion, computer vision can be particularly useful in test automation. From my experience, it excels in scenarios where traditional methods might fall short.

For instance, computer vision can detect visual discrepancies and layout issues that functional tests might miss. It’s great for verifying UI elements, ensuring that they appear correctly across different devices and resolutions. This is something traditional automation tools might struggle with, as they rely on element locators and may not handle visual changes effectively.

Additionally, computer vision can automate the testing of complex interactions and dynamic content, like verifying how a modal behaves or how content shifts with different screen sizes. This added layer of visual validation ensures a more comprehensive approach to testing, improving overall accuracy and reliability.

happy to help

[MattD_Burch]

In my opinion, some popular alternatives to Appium for mobile automation:

1. Selendroid: Mainly used for automating Android applications, especially for legacy apps.

2. Detox: A popular choice for React Native applications, offering fast and reliable end-to-end testing.

3. Espresso: Google’s tool for Android UI testing, known for its integration with Android Studio and robust performance.

4. XCUITest: Apple’s tool for iOS testing, integrated with Xcode and designed specifically for iOS applications.

From my experience, each tool has its strengths depending on the project requirements and technology stack. Using the right tool can streamline the automation process and improve test accuracy.

hope this was helpful to you

[heenakhan.khatri]

in my experience, AI tools can significantly enhance computer vision and other testing automation processes, especially when integrated into a DIY-style framework. From my experience:

AI-Powered Visual Testing: By incorporating AI, you can achieve more precise visual testing. AI helps in recognizing and adapting to changes in the UI, making it easier to spot visual issues that traditional methods might miss.

Automated Test Generation: AI can automate the creation of test cases based on real user behavior, which can be seamlessly added to your DIY framework. This saves time and ensures your tests cover more scenarios.

Predictive Analytics: AI tools can analyze historical test data to forecast potential issues. This allows you to focus your testing on areas with higher risks, improving efficiency.

Continuous Learning: AI can continuously improve by learning from new data. This helps in refining your tests over time, reducing false positives, and enhancing overall test accuracy.

Incorporating these AI-driven capabilities into your DIY testing framework can make your automation smarter and more adaptable, ultimately boosting its effectiveness.

hope this was helpful to you

[kusha.kpr]

In my opinion, both Java and Python are highly sought after, but they serve slightly different needs.

Java: It remains very popular in the enterprise world, especially for large-scale applications. It’s commonly used with frameworks like Selenium and JUnit, making it a go-to choice for many organizations.

Python: Its simplicity and readability make it a favorite for rapid development and testing. It’s widely used with frameworks like pytest and Robot Framework. Python’s growing popularity in data science and automation also adds to its demand.

In my opinion, choosing between Java and Python often depends on the specific requirements of the project and the existing tech stack. Both languages have strong communities and robust support for automation testing.

hope this was helpful to you

[neha.jlly]

I found that the development of robotics is particularly dynamic in several key areas:

Healthcare: Robotics is revolutionizing surgery with precision tools and automation in diagnostics and patient care. From my experience, this sector is rapidly evolving with innovations like robotic-assisted surgery and rehabilitation robots.

Manufacturing: Robotics continues to transform manufacturing with advanced automation and smart factories. Companies are integrating robots for tasks ranging from assembly to quality control, which is driving significant advancements in this field.

Agriculture: The use of robotics in agriculture, such as autonomous tractors and drones for crop monitoring, is gaining momentum. This sector is seeing innovative solutions for increasing efficiency and productivity in farming.

Logistics and Warehousing: Robotics is enhancing efficiency in logistics with automated sorting systems and delivery robots. This area is expanding quickly as companies look to streamline operations and reduce costs.

These areas are currently experiencing the most dynamic developments in robotics, driven by advances in technology and growing industry needs.

hope this was helpful for you

[anusha_gg]

In my experience, certain apps pose more challenges for automated or robotics-assisted testing, particularly those with complex interactions or dynamic elements. Here are a few examples and adaptations to consider:

1. Apps with Dynamic UI Elements: Apps that frequently change their user interface can be challenging. Adaptations include using AI-powered visual testing tools to handle variations and dynamic content.

2. Highly Interactive Apps: Apps with complex user interactions, such as games or simulations, may require more sophisticated test scripts. Incorporating behavior-driven development (BDD) and leveraging tools that support complex gesture recognition can be beneficial.

3. Apps with Custom Components: Applications with custom-built components or non-standard UI elements can be tough to test. In such cases, using tools with advanced object recognition capabilities and integrating them with custom test frameworks can help.

4. Multi-Platform Apps: Testing apps that run across multiple platforms (e.g., web, iOS, Android) requires ensuring consistency across environments. Utilizing cross-platform testing frameworks and managing environment-specific configurations can streamline this process.

Adapting your approach based on these considerations can help address the challenges associated with testing complex or challenging apps more effectively.

happy to help

• List item

[apksha.shukla]

I suggest that one of the best tools for vision-first test automation is Applitools Eyes. It offers advanced visual testing capabilities, including:

AI-Powered Visual Validation: It uses AI to detect visual differences, ensuring that UI changes are accurately captured and analyzed.

Cross-Browser and Cross-Device Testing: Applitools Eyes can test across various browsers and devices, maintaining consistency in visual appearance.

Integration with Major Frameworks: It integrates seamlessly with popular test automation frameworks like Selenium, Cypress, and Playwright.

Another notable tool is Percy, which also excels in visual testing with features like:

Snapshot Comparison: Percy captures visual snapshots and compares them against baseline images to detect changes.

Continuous Integration: It integrates well with CI/CD pipelines, automating visual tests as part of the development process.

Both tools are highly effective for vision-first test automation, with Applitools Eyes often preferred for its advanced AI capabilities and extensive integrations.

happy to help

[Apurvaugale]

There are some key challenges and limitations I’ve encountered in mobile test automation:

1. Device Fragmentation: Testing across a wide range of devices and operating system versions can be challenging. Ensuring consistent test coverage and managing different screen sizes and resolutions requires significant effort.

2. Performance Issues: Mobile devices have varying performance characteristics, which can impact test execution times and reliability. Managing test performance and dealing with slow or unresponsive devices can be a hurdle.

3. Network Conditions: Mobile tests often need to account for varying network conditions, such as slow or unstable connections. Simulating these conditions reliably in tests can be difficult.

4. Limited Access to Device Features: Some device features or sensors might be restricted or inaccessible through automation tools, making it hard to test certain functionalities fully.

5. User Interactions: Handling gestures and complex interactions (like swipes and pinches) can be tricky. Ensuring that automation tools accurately simulate these interactions often requires custom solutions.

6. App State Management: Managing different app states and ensuring consistent behavior across test runs can be complex, especially when dealing with background processes or interruptions.

Adapting strategies like using cloud-based testing services, implementing robust error handling, and utilizing device farms can help address these challenges effectively.

hope this was helpful

[archna.vv]

There are some of the top tools that I recommend based on my experience:

Applitools Eyes: This tool is highly regarded for its advanced visual validation capabilities. It uses AI to detect visual differences and integrates well with major automation frameworks like Selenium, Cypress, and Playwright.

Percy: Known for its visual review and snapshot comparison features, Percy captures visual changes and integrates smoothly into CI/CD pipelines, making it a strong choice for visual testing.

Testim: Testim offers AI-driven visual testing and can handle complex UI changes and dynamic content effectively. It provides easy-to-create and maintain test cases with a focus on visual elements.

Selenium with Visual Testing Plugins: Combining Selenium with plugins or extensions like SikuliX can enhance its visual testing capabilities. This approach can be useful if you’re already using Selenium and need additional visual validation.

Applitools Ultrafast Grid: This service allows for parallel visual testing across multiple browsers and devices, leveraging Applitools’ AI for accurate visual validation.

These tools are well-regarded for their ability to handle visual testing challenges, ensuring that UI changes and visual consistency are thoroughly validated.

hope this was helpful

[Ariyaskumar]

For choosing a tool for test automation, the “best” tool often depends on your specific needs and context. Here’s a personalized look at some top options based on their strengths:

1. Selenium:

Strengths: It’s a versatile tool for web application testing and supports multiple browsers and programming languages. It’s ideal if you need a well-established, flexible framework for web automation.

Use Case: Best for comprehensive web automation across different browsers and platforms.

2. Appium:

Strengths: Excellent for mobile app testing, supporting both Android and iOS. It allows you to write tests in various languages and integrates well with other tools.

Use Case: Ideal if your focus is on mobile applications and you need cross-platform testing.

3. Cypress:

Strengths: Known for its speed and ease of setup, it provides a rich debugging experience and is designed for modern web applications.

Use Case: Suitable for front-end testing with a focus on developer productivity and quick test feedback.

4. Testim:

Strengths: Offers AI-driven visual testing, which is great for handling complex UI changes and dynamic content.

Use Case: Ideal if you need robust visual testing and easier test maintenance with AI support.

5. Applitools Eyes:

Strengths: Provides advanced visual testing with AI, integrating well with various frameworks. It’s especially effective for ensuring visual consistency across different platforms.

Use Case: Best for scenarios where visual accuracy is crucial and you need comprehensive visual validation.

In my experience, each tool has its own strengths, and the choice largely depends on whether you need web, mobile, or visual testing capabilities. Evaluating your specific requirements and the context of your projects will help determine which tool is the most suitable for your needs.

hope this was helpful

[arpanaarora.934]

It has several benefits, based on my experience.

1. Enhanced Accuracy: Vision-first tools focus on how the application appears visually, which helps in catching UI discrepancies that might be missed by traditional element-based tests.

2. Adaptability to Dynamic Content: These tools handle dynamic UI elements and content changes more effectively, as they compare visual snapshots rather than relying on static locators.

3. Better User Experience Validation: By testing what users actually see, vision-first tools ensure the application’s visual and functional elements meet user expectations and design standards.

4. Simplified Test Maintenance: Vision-first testing reduces the need for frequent updates to test scripts caused by changes in element locators, making maintenance easier.

5. Consistency Across Platforms: Vision-first tools often support testing across different browsers and devices, ensuring that visual consistency is maintained everywhere.

Overall, this approach addresses common issues such as UI discrepancies, dynamic content handling, and test maintenance, leading to more reliable and user-focused test automation.

hope this was helpful to you

[Asheenaraghununan]

Here are some notable developments in performance, accessibility, and other testing areas:

1. Performance Testing Tools:

Advanced Load Testing: Tools like JMeter and Gatling have integrated advanced features for simulating complex load scenarios and analyzing performance metrics more effectively. They now offer enhanced support for cloud-based load testing and real-time performance monitoring.

Real User Monitoring (RUM): Tools like New Relic and Dynatrace provide real-time insights into how actual users experience performance, allowing for more accurate performance optimization and troubleshooting.

2. Accessibility Testing Tools:

AI-Powered Accessibility Testing: Tools like Axe and Lighthouse have incorporated AI to automatically detect and report accessibility issues, improving the accuracy and comprehensiveness of accessibility audits.

Continuous Accessibility Integration: Some tools now integrate seamlessly into CI/CD pipelines, enabling continuous accessibility testing throughout the development process and ensuring that accessibility standards are met from the start.

3. Security Testing Tools:

Automated Vulnerability Scanning: Tools like OWASP ZAP and Burp Suite have advanced in automating vulnerability detection and providing detailed insights into potential security risks in real time.

DevSecOps Integration: Security testing tools are increasingly integrating into DevSecOps practices, allowing for continuous security testing and vulnerability management throughout the development lifecycle.

4. API Testing Tools:

Enhanced Mocking and Virtualization: Tools like Postman and WireMock now offer more advanced capabilities for mocking and virtualizing APIs, facilitating more comprehensive and flexible API testing scenarios.

Automated Test Generation: Advanced tools can now automatically generate test cases based on API specifications, improving test coverage and efficiency.

These advancements across different types of testing tools reflect the growing need for more sophisticated, integrated, and automated solutions to address the evolving challenges in software testing.

hope this was helpful

[suneelak.673]

As per learning, computer vision can be highly beneficial if you’re aiming to stay ahead in the industry, especially if you’re involved in test automation and related fields. Here’s why:

1. Enhanced Test Accuracy: Understanding computer vision can significantly improve the accuracy of visual testing. It enables you to leverage advanced techniques for detecting UI changes, ensuring your tests are more reliable.

2. Adapting to Industry Trends: As automation and AI-driven testing continue to evolve, having a solid grasp of computer vision will help you stay at the forefront of these trends. It allows you to implement and understand cutting-edge technologies that are increasingly being integrated into testing frameworks.

3. Solving Complex Challenges: Computer vision knowledge equips you with the skills to tackle complex testing scenarios, such as dynamic content and intricate UI elements, which are common in modern applications.

4. Leveraging AI Capabilities: Many vision-first tools and frameworks rely on computer vision principles. By learning computer vision, you can better understand and utilize these tools, making you more adept at integrating them into your testing strategies.

5. Career Advancement: Expertise in computer vision can open up new opportunities in roles that focus on advanced testing techniques and AI-driven automation, positioning you as a valuable asset in the field.

Incorporating computer vision into your skill set aligns with industry advancements and enhances your ability to handle modern testing challenges effectively. It’s a strategic move if you want to lead in automation and stay ahead of industry trends.

happy to help

[isha.tantia]

By applying a top-down approach using cameras and sensors for 3D device or game testing can indeed be effective. Here’s how it can be beneficial:

1. Comprehensive Coverage: A top-down approach allows you to capture a full view of the 3D environment, which is useful for validating the overall spatial layout and interactions within the game or device. This method helps ensure that elements are positioned correctly and behave as expected from various angles.

2. Realistic Testing: By using cameras and sensors, you can simulate real-world conditions and user interactions more accurately. This helps in identifying issues that might not be apparent through traditional testing methods, such as object placement errors or visual inconsistencies.

3. Enhanced Accuracy: Cameras and sensors provide detailed data that can be used to verify the precision of 3D models and animations. This approach helps in detecting subtle defects or deviations from the expected behavior that could affect the user experience.

4. Efficient Automation: Integrating cameras and sensors into your test automation framework can streamline the testing process. Automated visual and spatial analysis can be performed, reducing manual effort and increasing testing efficiency.

5. Advanced Interaction Testing: For games or devices with complex interactions, a top-down approach can be used to capture and analyze user interactions in a 3D space, ensuring that all elements function correctly in a dynamic environment.

In summary, a top-down approach using cameras and sensors is a valuable technique for 3D device and game testing. It provides comprehensive coverage and realistic testing conditions and enhances the accuracy and efficiency of your testing efforts.

hope this was helpful

[smrity.maharishsarin]

Yes, testing can be used to test computer vision systems, which are integral for interpreting and processing visual information in various applications. Here’s how it can be effectively applied based on my experience:

1. Validation of Visual Recognition: Automated tests can verify the accuracy of computer vision algorithms in recognizing and processing visual data. This includes checking if the system correctly identifies objects, faces, or text within images or video streams.

2. Regression Testing: Automated testing can ensure that updates or changes to the computer vision algorithms do not introduce new issues. Regression tests can validate that existing functionalities are preserved and that the system continues to perform as expected after modifications.

3. Performance Metrics: Automated tests can measure performance metrics such as processing speed, accuracy, and response time of the computer vision system. This helps in assessing whether the system meets the required performance criteria.

4. Edge Case Testing: Automated testing can simulate a wide range of scenarios, including edge cases that might not be feasible to test manually. This helps in evaluating how well the computer vision system handles unusual or unexpected visual inputs.

5. Integration Testing: Automated tests can ensure that the computer vision component integrates seamlessly with other parts of the application. This includes checking the data flow between the computer vision system and other modules and verifying that the system behaves correctly within the larger application context.

6. Continuous Integration/Continuous Deployment (CI/CD): Automated testing can be integrated into CI/CD pipelines to provide continuous feedback on the performance and accuracy of computer vision systems. This ensures that any issues are detected and addressed early in the development cycle.

In summary, automated testing is highly effective for validating and ensuring the reliability of computer vision systems used in apps. It allows for thorough testing of visual recognition, performance, and integration aspects, contributing to the overall robustness and accuracy of computer vision applications.

hope this was helpful