Rajneesh's Logbook

- Testing AI requires a fundamental shift in mindset - Stree Testing an AI Model differs from traditional Load Testing - Toolkit for AI Testing - New testing techniques to transition to AI Testing Link

Vibe coding is rapid, AI-assisted development and has introduced a fundamentally software risk of subtle defects like inconsistent validation and security oversights. For testers, it means a strategy shift from scrutinizing implementation details to continuous behavioral verification and 'shifting wide'.  ​Success for testers will require prioritizing contract and integration coverage, leveraging AI for test generation, and strictly enforcing non-functional requirements of performance and security. QA must evolve into a high-speed steering system, and ensuring that rapid iteration methodology does not compromise user trust.  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...