Start with the first failing test or build step, capture its exact command and error, and reproduce it before changing code. Then make the smallest correction supported by the evidence, rerun the relevant checks, and review the patch for the behavior the project actually needs. A green test run is useful, but it does not by itself prove an AI-generated change is correct or fits the project.
1. Capture the failure before editing
Record the exact test name or build step, the command that ran, the first meaningful error, and whether the failure happened locally or in continuous integration (CI). Keep enough surrounding output to see which operation failed and what preceded it; changing code before preserving this evidence can make the original problem harder to diagnose.
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For a failed GitHub Actions run, open the failed workflow, identify the job and step that stopped, and inspect that step’s logs. If ordinary logs do not explain the failure, GitHub documents downloading the log archive and enabling additional debug logging: GitHub Actions workflow run logs.
2. Reproduce the problem with the project’s own checks
Use the commands documented in the repository, such as its README or contribution guide, rather than guessing a test runner or build command. There is no universal command that applies to every language and project. Begin with the smallest relevant check that reproduces the failure; once you have a focused result, run the broader checks appropriate to the change.
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GitHub’s review guidance puts functional checks first: “Always run automated tests and static analysis tools first.” It also recommends checking compilation, tests, warnings, and errors. See GitHub Docs: Review AI-generated code.
3. Classify what the output actually shows
Describe the observed symptom before settling on a cause. The first useful failure may point to one of several different problems:
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- Compilation or type error: The project cannot build the changed code as written. Start with the reported file, location, and diagnostic.
- Failing assertion: The code ran, but the observed result differed from what the test expected. Compare the expected and actual values and trace the relevant behavior.
- Missing dependency or configuration: The run reports an unavailable package, setting, credential, or other required input. Check the project’s documented setup and the environment where the failure occurs.
- Environment-specific failure: The same check behaves differently on different machines or between a local run and CI. Compare the commands, configuration, available dependencies, and relevant logs in both environments.
- CI job or workflow-step failure: A job or step may fail before the changed code is exercised. Use the failed step’s output to distinguish a workflow problem from an application or test failure.
These are useful diagnostic categories, not a ranking of the most common causes. Let the error and logs determine which branch to investigate.
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First establish whether the failure is reproducible locally, appears only in CI, or occurs intermittently. Compare the local command and output with the corresponding CI job and step. A local pass alongside a CI failure is evidence of a difference to investigate, not proof that either environment is wrong. If the CI log is too sparse to identify that difference, use the workflow’s additional logging or downloadable log archive.
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5. Make a narrow, evidence-based correction
Use the first meaningful failure to locate the code path or assumption that needs attention. Change the smallest relevant part, then check whether that change addresses the expected behavior as well as the reported symptom. Avoid altering unrelated behavior or weakening, deleting, or rewriting a test solely to make the run pass: a green check is only useful when the checks still test the intended outcome.
6. Validate the patch and review its intent
- Rerun the focused test or build step that first failed.
- Run the project’s relevant broader tests and compilation or build checks.
- Run the static analysis tools the project uses, and inspect any new warnings or errors.
- Review the complete diff, including test changes, against the expected behavior and the project’s architecture.
Passing checks establish that the patch cleared those checks; they do not establish that the code fits the project’s design or fulfills every intended requirement. Review the implementation and its surrounding context, not just the final test status. GitHub’s guidance on reviewing AI-generated code discusses both functional checks and contextual review: Review AI-generated code.
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7. Give the coding assistant enough context for another attempt
If you ask an AI coding assistant to debug or revise the change, provide the exact error, the failing test or build step, the relevant function, and the useful portion of the log. State the expected behavior and the project checks it should run. A request such as “fix the tests” leaves the assistant to guess which behavior matters; a precise failure and clear repository-specific instructions make the task more concrete.
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GitHub’s debugging guidance recommends including specific functions, error messages, or logs when asking for help: Getting started with GitHub Copilot. Its cloud-agent documentation also recommends clear instructions for validating changes: About GitHub Copilot coding agent.
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