QA Metrics
Test automation has upfront costs (writing, infrastructure) and ongoing costs (maintenance) set against time savings (faster test execution, fewer manual regression cycles). The ROI equation is straightforward once you have the right inputs. This post gives you the formula, walks through a worked example, and shows how to calculate break-even.
QA Metrics
MTTD (Mean Time to Detect) measures how long bugs hide before being found. MTTR (Mean Time to Repair) measures how long fixes take. Both are lagging indicators of QA maturity — but both can be systematically reduced through specific practices. This post gives you the definitions, benchmarks, and reduction strategies. Key
QA Metrics
QA investment pays back 4–15× depending on when bugs are caught. Bugs found in production cost 30× more to fix than bugs found in requirements. This post gives you the formulas to calculate your actual ROI and the narrative to present it upward. Key Takeaways * The IBM Systems Sciences
QA Metrics
The DORA metrics measure software delivery performance across four dimensions. Elite teams deploy more often and break things less — because they invest in test automation. This post maps each DORA metric to specific testing practices and shows how to improve your scores. Key Takeaways * DORA's four metrics: Deployment
QA Metrics
Most engineering teams track too few QA metrics (just pass/fail) or too many (every metric their CI tool exposes). This post identifies the five metrics that actually predict QA health, explains what each one tells you, and gives you targets and measurement approaches for each. Key Takeaways * Test coverage