Software Testing Magazine: Load Testing, Unit Testing, Functional Testing, Performance Testing, Agile Testing, DevOps
AI-written code can sail straight through your CI pipeline — lint clean, tests green, coverage above threshold — and still ship a bug nobody thought to test for. This article breaks down why AI code passes Continuous Integration (CI) checks built for a different generation of bugs, and lays out a short list of concrete, budget-free gates that catch what your CI pipeline can’t see today.
Metrics exist to answer two questions: how good is the software we deliver, and is our testing approach actually working? AI-generated tests broke the old answers to both. QA metrics were never really about numbers. They exist to answer two questions. First, how good is the software we are delivering to customers? Second, is our testing approach actually working, or are we just going through the motions?
In this article, Mikhail Golikov, the sole QA on a seven-team backend e-commerce platform, explains where each regression check belongs, in the browser or at the API. He shares the rule he actually uses, two bugs that only one layer could have caught, and the realization that finally made his browser suite small and stable.
As AI makes test creation nearly effortless, software teams face a new challenge: determining which tests actually improve quality and which simply add noise.
In this article, Mikhail Golikov, the sole QA on a seven-team backend platform, explains how he turned a drawer full of unrun Postman collections into committable pytest suites, walks through the conversion request by request, and shows the folder-scoped command-line tool he built so one team’s pattern could scale to seven.
In most of your software testing activities, you need data. Sometimes you can rely on a small sample, but if you want to perform some load testing or if you want to test a feature that needs to produce a multipage invoice, then you start to need more than just two or three occurrences. Test data generators are tools that can help you in this task with the automatic generation of hundreds or thousands of customers, products or accounts items with different attributes for their id, email, name, etc.
As distributed systems are too complex for deterministic testing, AI can help. In this article, Naveen Prakash proposes an approach based on the ideas of chaos engineering and AI-assisted testing. The focus shifts from testing individual pieces to understanding what happens when many services run together under unpredictable conditions.
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