AI For Test Generation: Where It Helps And Where It Lies

<h3> Intro </h3> <p>There's a pitch behind every "AI writes your tests too" workflow: more coverage, less manual toil, a safety net that used to take a sprint now takes

<h2> The Asymmetry of Change: Why Your Tests Are Looking the Wrong Way </h2> <p>A passing build is often treated as a certificate of correctness. In reality, it's a narrow

<p>The more I use AI tools like Claude Code, the clearer it becomes: engineering skills are what make AI output worth shipping.</p> <p>AI makes writing code faster. But shipping

<p>Last month I asked a senior candidate the same question I ask everyone. "Tell me about a flaky test you fixed and how you fixed it."</p> <p>She paused and said, "I just added a

<h1> A green test suite proves less than you think </h1> <p>The test that scared me was the one that passed.</p> <p>I had an integration test for a routing agent, the kind that

<p>CI is red. You open the PR. Now what?</p> <p>You click the failing workflow. You read the logs. You open the trace viewer in a separate tab. You cross-reference the error with

<p>While exploring AI-powered test automation, I came across different approaches from platforms like mabl, Testim, and Functionize.</p> <p>mabl focuses strongly on AI-assisted

<p>You wouldn't ship code without tests. But most AI agents ship with nothing — a handful of manual prompts in a notebook, a screenshot of "it worked once," and a prayer that