Testing Visibility
Most teams know that coverage alone does not show whether tests work. Fewer can see how their tests behave across environments over time. This series of posts builds the argument step by step.
Read in order
- Why Testing Matters More in the AI Era makes the case for test discipline when AI writes the code.
- The Testing Antipatterns AI Exposes lists six practices that fail under acceleration.
- Measuring What Matters looks at execution patterns, flakiness and failure analysis.
- The Testing Visibility Gap describes five visibility gaps and what closing them requires.
A note on Obvyr
I founded Obvyr, a test observability product. The problem in these posts is the one it addresses, so I declare the interest here. My advice work on delivery measurement is separate: see operational excellence through data.
All writing on this theme
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The Testing Visibility Gap: Why Most Teams Are Flying Blind
: Most teams know what to measure about testing but cannot see enough to do it. Five visibility gaps, from execution tracking to cross-team data. -
Measuring What Matters: How to Evaluate Testing Effectiveness in AI-Assisted Development
: Coverage does not show whether tests work. Measure test effectiveness through execution patterns, flakiness, failure analysis and local versus CI differences. -
The Testing Antipatterns AI Exposes: Common Practices That Fail Under AI Acceleration
: Six testing antipatterns that AI-assisted development exposes, from skipped test-first work to coverage theatre and flaky tests, with questions to spot them. -
Why Testing Matters More in the AI Era: The Critical Discipline We Can't Afford to Skip
: Why testing discipline matters more when AI writes the code: how test-first work guides AI, how the velocity trap catches teams, and how to keep verifying.