AI testing tools are becoming part of the normal QA conversation. They can generate test cases, help with automation, summarize bugs, review logs, find visual changes and even suggest edge cases. For ...

From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing. The integration of artificial ...

AI-driven testing is reshaping software quality assurance with intelligent, self-adapting automation. By leveraging machine learning and natural language processing, it enables codeless testing, ...

The automation waves of the past rewarded companies with the best systems, not the most robots, and AI will be no different.

Quality Assurance (QA) models built for slower release cycles are struggling to keep pace with modern enterprise systems powered by microservices, APIs, cloud infrastructure, and continuously evolving ...

While AI automates the bulk of test generation, teams must still budget substantial engineering time for human review and ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. AI makes the first 80% of development feel ...

A VentureBeat Pulse survey of 157 enterprises shows AI agents gaining autonomy even as trust in the evaluations meant to ...

A comparison of eight AI-powered requirements management platforms for 2026, from NLP-based quality analysis and automated test generation to live traceability scoring, covering Jama Connect, IBM ...

The latest trends in software development from the Computer Weekly Application Developer Network. As we collectively build the new era of agentic AI automation services, we will need to constantly and ...

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Software testing has always faced the same basic tension: thoroughness takes time, and time is ...