Systems | Development | Analytics | API | Testing

We’ll always remember 2025 as the year AI code assistants went big. Copilot, Cursor, Claude, Windsurf, whatever. Developers went from mistrusting these tools, to being expected to turn over much of their coding labor to them. Even if, according to an extensive Stack Overflow survey, only 3 percent of professional developers say they ‘highly trust’ AI coding tools.

After some testing and bug fixes, one common question always remains: how do teams make sure that those defects are truly resolved, and no new regressions creep in? That’s where retesting testing becomes vital. Retest testing forms a very important aspect of any QA cycle, ensuring that the reported defects are fixed and working correctly before the software moves to production. Without it, even simple patches can introduce silent issues into live environments.

For years, APIs have been designed with one primary audience in mind: developers. The focus has been on making APIs discoverable, consistent, and easy for humans to consume. But in the AI era, a new audience has arrived: AI agents. As detailed in SmartBear’s newly published AI-Enabled API Lifecycle Report, this shift demands that organizations rethink how APIs are designed, tested, and managed to serve both human developers and intelligent systems effectively.

As coding has become more autonomous, so has code-based testing. AI agents can now write functions, generate code-based tests, and validate logic in the same workflow. But the other half of the testing equation, the system-level validation of non-code-based testing, hasn’t kept up. That disconnect is becoming one of the most critical constraints in modern software delivery.

The updates we make to Perforce ALM are deeply influenced by the real-world challenges our users face. We understand that those who work in highly regulated industries need trustworthy tools that deliver precision, traceability, and efficiency. That’s why we plan each round of ALM enhancements based directly on user comments and suggestions. This quick guide will look at recent updates around tagging and filtering and how to leverage these features for a more intuitive and efficient user experience.

Hi everyone, I’m Elly, Katalon Community Specialist by title, tester at heart. I spend a good chunk of my time researching around the Katalon Forum reading bug reports, answering questions, and watching how this brilliant community solves problems together. Over the past month, I’ve been tracking what everyone’s been talking about: the tricky bugs, the clever fixes, and the shout-worthy contributions. This post is my way of sharing that back with all of you.

What if a single, untriggered logical flaw could compromise an autonomous vehicle’s braking system or ground a commercial airliner? The stakes are unbelievably high with safety-critical software. Traditional code coverage metrics, however, often fail to test the subtle, complex dependencies within a single decision. Knowing that a line of code executed isn’t the same as knowing the logic works.