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To analyze failing smoke_test and e2e_test results, you provide the test output files to the incident_repair.py tool. It parses the logs to identify unresolved incidents and generates structured remediation guidance including inferred_next_action, patch_candidates, and verify_steps.

The best way to generate patch proposals for incident repair scenarios is to parse failing test outputs through incident_repair.py. This process yields structured patch candidates and verification steps, enabling semi-automated failure analysis to reduce resolution time.

Semi-automated failure analysis works by interpreting failed test logs to identify unresolved incidents, then outputting an execution plan. This plan includes an inferred next action, proposed patch candidates, and verification steps for safe application.

Yes, you need to provide smoke_test and e2e_test output files as input. The incident_repair.py tool parses these specific test outputs to identify failures and formulate the proposed remediation actions and patch candidates.

Yes, you can review patch candidates before applying them. The system provides structured guidance and verify_steps, ensuring you evaluate the proposed patches and complete a validation stage before deciding to apply any changes.

If smoke_test failure analysis does not identify the root cause, you should manually review the raw test logs alongside the inferred_next_action. The tool provides patch candidates based on parsed outputs, but complex incidents may require additional manual investigation beyond the automated proposals.