Building Resilient Quality Engineering Teams: Exploring Emerging Trends and Best Practices

About The Video

Watch this episode of XP Series featuring 𝐏𝐮𝐫𝐯𝐚 𝐉𝐚𝐢𝐧, Director of Quality Engineering, Veeva Systems.

🎙️In this episode of the XP Series, Purva explores the transformative impact of AI/ML in testing, the evolving paradigms of Shift Left and Shift Right, and the crucial role of Observability in ensuring continuous quality.

You will also learn actionable insights into fostering a culture of quality, developing strong QE leadership, promoting effective collaboration, and investing in continuous learning within your teams.

Video Chapters

00:00 Welcome

02:00 The Evolution of Quality Engineering

08:14 How can QA teams validate AI-driven features while using AI for testing

20:13 Bridging Shift-Left and Shift-Right: Practical Testing Strategies for Teams

25:28 Advice for Teams Struggling With Observability Data

31:09 Balancing Speed, Thoroughness & Automation Strategies

35:07 Advocating Quality Under Delivery Pressure

42:34 Practical Ways to Break Down QE and Tech Team Silos

49:32 Future Trends Shaping Quality Engineering

53:14 Wrapping Up

Key Topics Covered

Introduction to Shift-Left Testing:

The video discusses the concept of shift-left testing in the context of AI-first product development. This approach aims to accelerate product development, especially in terms of quality assurance, by integrating testing early in the software development lifecycle (SDLC).

AI-First Product Development:

AI-centric development is revolutionizing product life cycles. It accelerates phases such as ideation, design, development, and testing. AI tools are significantly speeding up tasks like UX/UI design, coding, and test case generation.

Challenges in Quality Assurance:

While AI accelerates development, it introduces challenges, particularly in the testing phase. Testers are not only responsible for testing new features but must also ensure the stability of the entire product, requiring a more dynamic approach to testing.

Integration of AI in Testing:

AI is enhancing test coverage by automatically generating tests, including unit tests and end-to-end tests. However, the quality of these tests must be continuously assessed through tools like mutation testing to ensure they are robust and effective.

Mutation Testing:

The video emphasizes the importance of mutation testing, which checks whether the existing tests can catch errors introduced by modifications in the code. Mutation coverage is an important measure of test quality alongside basic test coverage.

Continuous Testing:

Continuous testing is crucial in high-velocity environments. The video advocates for embedding testing throughout the SDLC and using automated testing pipelines that trigger tests with every commit to ensure continuous quality.

AI's Role in Automation:

AI is making test automation more accessible, even for business users, through natural language processing (NLP). AI can automate test creation, execution, and debugging, potentially reducing the barrier for non-technical users to engage with the testing process.

Guardrails and Standards:

To avoid suboptimal AI-generated code, organizations must establish clear coding and testing standards. These standards should be stored in formats that AI can easily understand (e.g., markdown) to ensure consistency across the development process.

Future of AI in Testing:

Looking ahead, AI is expected to further reduce the need for manual testing by automating more mundane tasks. However, human expertise will remain essential for exploratory testing and addressing complex scenarios that AI might not fully capture.

Test Coverage and AI-Driven Insights:

AI-driven tools can analyze defects based on historical data, prioritizing which tests to run based on changes to the codebase. This helps reduce the volume of regression tests while maintaining confidence in the product's quality.

Impact on Test Automation in Enterprises:

The video highlights the importance of integrating test automation into CI/CD pipelines and ensuring that testing is not a siloed process. Automation of testing processes, combined with AI, is expected to improve efficiency and speed across enterprises.

Adapting to AI-Driven Testing:

Organizations must train their teams to embrace AI tools, transitioning from traditional testing methods to AI-assisted automation. This shift requires both technical and strategic adjustments to maintain the balance between speed and quality.

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