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Start with the first failing test or build step, capture its exact error and command, and reproduce the failure using the repository’s documented checks. Then make the smallest change that addresses the evidence, rerun the relevant checks, and review whether the patch still meets the intended behavior. A green test run is useful evidence, not proof that the code is correct for the project.
What to capture before changing code
Record the failure while it is still reproducible. Keep the exact test name or build step, the command that ran, the first meaningful error, and enough surrounding output to understand the context. Note whether it failed locally, in continuous integration (CI), or intermittently.
- Test failure: Save the failing test name, assertion output, and relevant test log.
- Build or compilation failure: Capture the first useful compiler, type-checker, or build-system error rather than only the final generic failure message.
- CI failure: Identify the failed workflow job and step, then inspect that step’s logs. GitHub documents how to inspect workflow logs, download a log archive, and enable additional debug logging when the ordinary output is insufficient: GitHub Actions workflow run logs.
Do not start by changing several files or asking an assistant to “fix everything.” Those moves can obscure which condition caused the failure.
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Reproduce the failure with the project’s own checks
Use the commands and setup documented by the repository. There is no universal test or build command that applies across languages and projects, so avoid substituting a guessed command for the project’s documented workflow.
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- Run the narrowest relevant test or build check first, using the project’s documented command.
- If it fails, compare the output with the original failure and keep the error context.
- After a focused check passes, run the broader relevant test suite, compilation or build, and static analysis as appropriate for the project.
GitHub’s guidance for reviewing AI-generated code says, “Always run automated tests and static analysis tools first.” Its functional-check guidance also recommends checking compilation, tests, warnings, and errors: GitHub Docs: Review AI-generated code. Treat new warnings as information to investigate, not as proof that the build is safe simply because it completed.
Classify the failure from its output
Use the symptom and log to decide what to inspect next. These categories are practical ways to organize diagnosis, not claims about which problems are most common.
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| Observed symptom | Next evidence to inspect |
|---|---|
| Compilation or type error | The first specific compiler or type-checker message, the referenced file and location, and the code path involved. |
| Failing assertion or test | The expected and actual values, the test’s setup, and the behavior the test is intended to verify. |
| Missing dependency or configuration value | The repository’s setup instructions, dependency declarations, and the failing command’s configuration context. |
| Environment-specific failure | Whether the same documented command fails locally and in CI, plus relevant differences in configuration or runtime context. |
| CI job or workflow-step failure | The first failed job and step, their logs, and whether the failure is in the code check or in the workflow environment. |
A category narrows the investigation; it does not establish a cause by itself. Follow the concrete message and reproduce the behavior before editing.
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Compare the local and CI failures rather than assuming the code or the CI system is at fault. Check whether the same repository command was run and whether the first failed test or step is actually the same. A CI run can reveal a setup, configuration, or environment difference that a local run does not exercise.
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- Open the failed CI run and identify its first relevant failed job and step.
- Read that step’s logs and compare the command and error with the local run.
- If the logs do not explain the failure, use the platform’s documented options for more detailed logging or download the log archive. GitHub describes both options in its workflow log guidance.
- Reproduce locally using the repository’s documented setup and command where possible; do not assume your local environment is equivalent to CI.
Make a narrow correction, then verify the patch
Use the first failure to identify the code path or assumption that needs attention. Correct that behavior with the smallest change supported by the evidence. Avoid unrelated cleanup while diagnosing, and do not remove or weaken a test merely to make the check pass: doing so can erase the signal without fixing the underlying behavior.
After editing, validate in layers:
- Rerun the failing test or build step.
- Run the project’s relevant broader tests and compilation or build checks.
- Run the project’s static analysis, where applicable, and inspect new warnings and errors.
- Review the diff, including test changes, against the intended behavior and the project’s architecture.
GitHub’s review guidance calls for both functional checks and review of the project context; passing tests alone does not show that a change fits the intended behavior or architecture: GitHub Docs: Review AI-generated code.
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Give an AI coding assistant useful debugging context
If you ask the assistant for another attempt, give it evidence and boundaries rather than a broad request. Include the exact error, failing test, relevant function, and a concise excerpt of the pertinent logs. State the expected behavior and ask it to run the repository’s documented validation checks.
- Point to the specific failing function or test instead of asking it to review the whole codebase without direction.
- Paste the actual error and relevant log context; do not paraphrase away details such as the failed assertion or build step.
- Explain the expected behavior and any repository-specific constraints that matter to the fix.
- Ask for a focused patch and the checks used to validate it, then inspect and verify the resulting diff yourself.
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