Rajneesh's Logbook

AI is not a shortcut for generating final outputs, instead teams should treat it as a 'thought partner' where AI-generated suggestions are rigorously tested and validated by human decision makung. By selecting appropriate use cases and embedding AI into existing workflows with clear guardrails in place, leadership can move the team away from mechanical tasks toward deeper risk analysis and more effective testing. (And equally applicable to all other teams as well - developers, architects, business analysts, product owners...!) Link

AI is a tool, not a solution for quality itself. Quality in loop comes from the human ability to question, validate, and refuse/reject to outsource judgment to a robot. AI supports the process with ideas, heuristics, and note-taking, but humans retain ownership of the charters, decisions, and debriefs. Testers add value most when they act as the accountable layer in this loop!  Testing Podcast

Microsoft recently launched Azure App Testing, a unified hub combining Azure Load Testing and Playwright Testing. The platform leverages AI-powered tools and VS Code extensions to automate browser tests and simulate global traffic, simplifying resource management and billing. Check it out at InfoQ

Beth M worked with AI tools for testing and concludes that tools like Cursor and Fine.ai can significantly accelerate the development of test automation frameworks. However, human angle will continue to lay the important role to ensure the accuracy and reliability of the generated code. She recommends to continue using such tools for these can offer substantial time savings, but points out the lack of intuitiveness and a learning curve for users accustomed to traditional IDEs.   Link

Latest World Quality Report (2024-25) is out and as usual, it examines current Quality Engineering trends and challenges across various industries. This year, report highlights the increasing adoption of Gen AI in QE, emphasizing its impact on test automation and data quality. Key Highlights: 1. Gen AI Integration - Organizations are actively integrating/planning Gen AI into Quality Engineering processes, with a focus on test automation, data analysis, and eventually into defect prediction. 2. Intelligent Product Validation - Validating intelligent and connected products is becoming increasingly important, requiring specialized testing approaches and skills. 3. Prioritizing Data Quality - Importance of data quality is still underestimated by many organizations, thus, posing challenges for AI implementation and overall IT efficiency. 4. Sustainability Focus - Organizations are actively working to integrate sustainability into Quality Engineering, including defining Gre...

1. Key features to consider when selecting modern testing tools, such as automation efficiency, integration with CI/CD pipelines, cross-platform support, AI-powered insights, comprehensive reporting, and ease of use   Link 2. Why do we refer to the real requirements for a product as ‘non-functional’ requirements?  Link 3. Integrating Application Performance Management (APM) tools can enhance load testing by providing deeper insights into CPU usage, memory consumption, garbage collection, thread pool utilization, latency, Apdex scores, and database query performance. This integration enables a more thorough understanding of system behavior under load, facilitating the identification and resolution of performance bottlenecks. Link