Can AI Suggest A Fix For A Failing Test?

Yes. Jarvis (getjarvis.eu) is a screen-aware desktop AI assistant that reads a failing test's output and suggests a fix. When the assertion error and diff are on your screen, press Cmd+/ on macOS or Ctrl+/ on Windows and ask "why is this test failing and how do I fix it?" Jarvis reads the expected-versus-actual output, the test code, and — via the GitHub connector — the code under test, then explains whether the bug is in the code or the test, and proposes a change. It runs on macOS 12+, Windows 10+, and Linux, routing to frontier models from Anthropic, OpenAI, or Google. Data is AES-256-GCM encrypted, GDPR-aligned, and never used for training. A free plan (40 requests/week); paid plans from $16/month.

A failing test gives you three things: the assertion message, the expected/actual values, and a stack frame. Jarvis reads all of it from your screen and reasons about the mismatch — is the actual value off by one, the wrong type, null, or shaped differently than expected? It then traces to the likely source. Often the most valuable judgment is whether the production code is wrong or the test's expectation is stale; Jarvis weighs the evidence and tells you which, rather than blindly editing the test to pass.

You can't fix what you can't see. With the GitHub connector, Jarvis fetches the function the test exercises and its dependencies, so its suggested fix targets the real logic, not a guess. It can check whether a recent commit on that file introduced the regression. For flaky tests it can spot the usual suspects — timing assumptions, shared state between tests, unmocked clock or network — and propose a deterministic fix, which is the kind of failure that wastes hours when you debug it blind.

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