What this measures
You will instrument three moments engineers already know: reading PRs, drafting remediation plans, and walking QA through fixes. Run each scenario twice to see whether AI actually reduces review minutes without spiking TLX.
Quickstart · Software Engineering
Measure AI code review, pairing, and testbench lift.
Use this quickstart to calibrate your first Analyzer pack as a software engineering lead. You will benchmark review loops, pairing, and defect triage so Δ and TLX become part of every retro.
Common pitfalls
- Comparing different PRs. Use one realistic diff for both runs.
- Accepting AI remediation plans verbatim. Reviewer minutes will spike later.
- Only reporting throughput. Pair Δ with TLX so fatigue isn’t invisible.
Three task examples
Task 1: Critical PR review
Review a risky diff manually, then with AI suggestions. Log reviewer minutes and defect flags.
Task 2: Remediation plan
Draft a rollback / fix-forward note twice. Capture where AI misses context.
Task 3: QA handoff
Explain the fix to QA. AI can summarize logs quickly, but you must verify accuracy.
Before you start
- Pick one diff or incident. Reuse it for both runs.
- List what “passed review” means (defect classes, test gates).
- Capture TLX immediately—engineer memory fades after the retro.
- Bring Δ + TLX into stand-up decks so stakeholders see the guardrails.
7-Step Evaluation Process
Follow our proven methodology for accurate AI evaluations
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📅 Your First Week Plan
Day 1:Complete manual baseline (Step 1)
Day 2:Review key resources and prepare AI tools
Day 3:Run AI-assisted evaluation (Step 2)
Day 4-5:Calculate metrics and review results (Steps 3-5)
Day 6:Share tiles with team (Step 7)
Day 7:Team retro and plan next experiment
Ready for the first pack?
Bookmark this quickstart. After each Analyzer run, drop the TLX snapshot and Overestimation Δ tiles into your stand-up doc so progress stays visible.