Smoke to avoid Fire
summary
Reused regression automation runs fast atomic checks via a Jenkins pipeline with agent monitoring and parallel executors, smoke-checking 21 applications with 40 atomic checks across SIT UAT Prod in 9 minutes and surfacing 33 environmental defects so far. The team documents tradeoffs: avoid UI‑heavy or bloated checks, prefer API/service-level probes, aim for 6–7 minute execution, add Grafana dashboards for trending, and explore an ML model to predict risky check‑ins.
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chapters (17)
- 0:00Introduction
- 1:33Smoke testing importance
- 3:40Analyst view on smoke testing
- 6:10Team goals for smoke tests
- 8:16Reference architecture framework
- 10:21Pitfalls in smoke testing
- 12:30Handling flaky infrastructure
- 13:55Success metrics for smoke tests
- 14:37Future smoke testing optimizations
- 16:42Q&A: Smoke test coverage
- 17:46Q&A: Smoke vs functional tests
- 18:50Q&A: Developer adoption
- 19:33Q&A: Selecting test cases
- 20:15Q&A: Business metrics reports
- 21:18Q&A: Automated vs manual duration
- 22:20Q&A: Smoke tests in production
- 23:04Q&A: Refactoring smoke tests
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