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Stable systems. Trustworthy AI. Safe automation. Reliable releases. Strong customer experiences. Software testing is entering a new phase. For years, QA teams have focused on functional testing, regression testing, automation, integration testing, and release validation. These practices remain important, but AI is changing how quality is achieved. Repetitive activities such as regression execution, test-data
A few weeks ago, I started exploring how AI could support backend automation testing using Antigravity. My goal was simple—reduce the effort involved in writing, maintaining, and scaling API tests while improving overall efficiency. I began by experimenting with backend test execution using REST Assured, with Antigravity assisting me in generating and structuring test logic.
From Exploration to Application: My Journey Building an AI-Assisted API Testing Tool Read More »
Introduction Production bugs are expensive—but the real cost isn’t just fixing them. It’s lost revenue, damaged trust, and engineering time spent firefighting instead of building. According to IBM’s Cost of a Data Breach Report (2023), issues caught in production can cost up to 15x more than those identified during development. The uncomfortable truth? Traditional debugging
AI Debugging in ADLC: Catching Production Bugs Before They Exist Read More »
