Codeceptjs Run Analysis

CodeceptJS Run Analysis

After npx codeceptjs run, trace artifacts land in output/. This skill reads them efficiently — right step, right file, right slice of a giant HTML snapshot — via bash tools rather than re-running through MCP.

No single end goal: pick the use case matching the situation. The foundations apply to all of them.

Foundations

• aiTrace must be on. Everything leans on output/trace_<TestName>_<hash>/trace.md. Confirm in the active config (run codeceptjs-fundamentals if unknown). Without it there are only screenshots and pageInfo dumps — suggest enabling and re-running before deep analysis.
• run_step_by_step is interactive only; aiTrace is the sole source of per-step files. Ad-hoc run_code / snapshot still produce single-shot bundles under output/trace_run_code_* / output/snapshot_*.
• Locate traces: reruns create new dirs — when unclear, most recent wins (ls -dt output/trace_*).
• Read trace.md first — it's the index linking each step to its artifacts.
  • Focus on the failed step; none marked → last step in the trace.
  • Multiple failures marked → the first is usually the cause; the rest cascade.

Artifact order for the focus step (NNNN_<step>)

Never read big files whole

• HTML snapshots: search with grep (text, class, aria-label, data-*) — line numbers + context flags keep matches readable.
• console.json: filter with jq, don't read every entry.
• Scanning many files: grep -l for filenames only.

Use cases

Verify a fix held

Locate latest trace → read trace.md → confirm no FAILED markers. Glance at console.json for warnings worth fixing while you're there.

Cluster errors across a CI batch

Extract failing-step lines from every trace.md, group by signature (grep + sort + uniq -c), rank by frequency.

• Same error in many tests = systemic (env var, auth, base URL, deploy regression) — fix root cause once, rerun the batch.
• Different errors per test = local — triage one at a time. Start with the most frequent root cause; rerun to see how many tests came back with it.

Diagnose flakiness

Rerun the same test 5–10 times; compare which step failed in each trace.

• Different step each run → timing, environment, external service
• Same step, different state → missing/wrong wait — diff the ARIA snapshots of that step between runs
• console.json differs between runs → transient backend/network errors
• Bounding box differs → layout reflow or late-loading content

Investigate a single failure

Failed-or-last step → ARIA + screenshot first, console second, HTML last (grep only). Form a hypothesis. Trace not enough / page needs live poking → hand off to debugging-codeceptjs-tests.

After analysis

• Systemic cause → fix root once (env, auth, deploy), not per test
• Locator drift → codeceptjs-exploration
• Timing/wait issue → Waiting guidance from fundamentals/writing skills; replace I.wait(N) with specific waitFor*
• Resists static analysis → debugging-codeceptjs-tests (live MCP loop)
• Clean pass → done, but glance at console.json anyway

Things to avoid

• Reading large HTML or console.json whole — grep / jq.
• Stopping at the last marked failure instead of the first.
• Triaging individual failures before clustering.
• Flakiness conclusions from a single run — needs 5+ reruns.
• Deleting output/ mid-investigation.

Related skills

• codeceptjs-fundamentals — what's configured, aiTrace -p overrides
• codeceptjs-exploration — locator drift fixes
• debugging-codeceptjs-tests — live loop + offline locator resolution via codeceptq