Section 013 · Chapter 2, From Tests to Release Evidence
Reproducibility: Logging the Right Things
Non-deterministic bugs are hard to debug unless Confidence Engineers capture the context around the failure.
reproducibilityconfidence engineerreproducibility logging right things
What to do
- Save the repo commit, branch, dependency lockfile, OS image, browser version, environment variables, test seed, exact command, failing output, screenshots, tool calls, files inspected, model version, prompt template, and diff.
- Define runnable checks that exercise reproducibility, confidence engineer, and reproducibility logging right things.
- Set acceptable outcomes and blocker failures for reproducibility, confidence engineer, and reproducibility logging right things before running the evaluation.
Evidence to preserve
- Save the repo commit, branch, dependency lockfile, OS image, browser version, environment variables, test seed, exact command, failing output, screenshots, tool calls, files inspected, model version, prompt template, and diff.
- Preserve the inputs, versions, configurations, raw outcomes, and results for reproducibility, confidence engineer, reproducibility logging right things needed to reproduce work on Reproducibility: Logging the Right Things.
- Report results for reproducibility, confidence engineer, reproducibility logging right things by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Expert logging distinguishes replay data from diagnosis data. Replay data tries to recreate conditions. Diagnosis data explains why the system behaved that way. Both should be privacy-aware, access-controlled, and tied to durable artifact IDs.
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Cite this page
Jason Arbon. "Reproducibility: Logging the Right Things." Testing AI Knowledge Edition, section 13.
https://jarbon.ai/testing-ai/knowledge/ch013-reproducibility-logging-right-things.html