Measuring Performance with Functional Tests: A Low-Effort Approach for High Impact
summary
Proposal shows that adding zero-budget timestamps to existing functional tests produces actionable metrics: record start/end times, add granular stopwatches, use median not mean, and ignore failing runs. It uses CI runners, simple spreadsheets or Grafana, and Slack warnings (not build blocks). Practical rules include baseline recalibration, bracketing DB noise, run 3–5 iterations per case, weight critical tests into a performance score, and thresholds (example: 2.2s baseline → 2.64s warning). Real deployments catch 20% regressions and surface issues early.
AI-written summary · about
chapters (12)
- 0:00Introduction
- 2:20Performance failure examples
- 9:51Why performance testing is neglected
- 12:25Turning functional tests into performance tests
- 15:39Ten tips for effective measurement
- 24:57Environment consistency and baseline
- 30:25Avoiding cold starts and unreliable data
- 32:59Visualizing and acting on performance data
- 35:49Cost of ignoring performance issues
- 37:36Q&A: Framework impact
- 39:46Q&A: Stress testing
- 41:15Q&A
related talks
49mPerformance Testing
38mCreating & Scaling Meaningful Performance Tests in CI
41mContinuous Performance Testing in DevOps
53mEarly Performance Testing: Finding News You Can Use
39mCreating & Scaling Meaningful Performance Tests in CI
01h 00mPerformance In Modern Times: An Evolutionary Journey
29mProgressive Testing to Meet the Performance Imperative
41mMartijn Goossens: Become a Performance Test Master - the Non-Technical Side of Things