The future of AI user testing will be transformative for web development, and getting in on the act early could pay dividends in getting ahead of your competitors.

What about test-flakiness, patterns, regression testing, test coverage, robustness, redundancies and developer satisfaction?

Learn practical tools and strategies to build smarter, reliable AI agents using DPVAL metrics and N8N workflows for better ...

Positive testing ensures core functionalities behave correctly and users can complete their intended tasks without disruptions. It establishes a baseline of reliability, confirming that the most ...

Human-in-the-loop (HITL) frameworks ensure human oversight stays in place. QA professionals should review AI-generated test ...

TestRail, the leading dedicated QA test management platform, announces the release of AI-powered Test Case Generation ...

AI agents keep learning and changing — figuring out how to version them is key to keeping things safe, reliable and ...

The modern enterprise landscape demands robust, scalable software solutions that can handle millions of daily requests while maintaining optimal performance and reliability. As organizations ...

Wang, S. (2025) A Review of Agent Data Evaluation: Status, Challenges, and Future Prospects as of 2025. Journal of Software ...

Good vision is essential for enjoying independence and, of course, the joys of life. However, more than 2.2 billion people are unable to do so due to having a vision impairment, which happens due to ...

Ramya Krishnamoorthy shares a detailed case study on rewriting Momento's high-performance data platform from Kotlin to Rust.