Performance Metrics for Test Evaluation

Engineers keep asking me the same question about LLM testing. "How do I know if my evaluation metrics are actually working?" Most teams run evaluations, see a score, and assume it means something. But they never validate whether their metrics catch real failures. Last month, a Series B company came to us after their "95% accuracy" RAG system hallucinated customer data in production. Their evaluation pipeline gave them a false sense of security. The problem? They never tested their tests. Here's what we do at Confident AI to validate evaluation metrics: Test against known failures. Take 10 to 20 examples where you KNOW the LLM failed. If your metrics don't flag them, they're broken. Create adversarial test sets. Build datasets designed to break your system. Your metrics should catch edge cases and ambiguous queries. Compare against human judgment. Have domain experts label 50 random outputs. If your metrics agree less than 80% of the time, you have a metrics problem, not a model problem. The meta-lesson: evaluation is only valuable if you can trust your evaluation. What's your approach to validating metrics?

💬 I get this question a lot in interviews: "What quality metrics do you track?" Here’s the basic version of my answer—it’s a solid starting point, but I’m always looking to improve it. Am I missing anything? What would you add? ✨ Engineering Level I look at automated test coverage—not just the percentage, but how useful the coverage actually is. I also track test pass rates, flake rates, and build stability to understand how reliable and healthy our pipelines are. ✨ Release Level I pay close attention to defect escape rate—how many bugs make it to production—and how fast we detect and fix them. Time to detect and time to resolve are critical signals. ✨ Customer Impact I include metrics like production incident frequency, support ticket trends, and even customer satisfaction scores tied to quality issues. If it affects the user, it matters. ✨ Team Behavior I look at where bugs are found—how early in the process—and how much value we get from exploratory testing vs. automation. These help guide where to invest in tooling or process improvements. 📊 I always tailor metrics to where the team is in their journey. For some, just seeing where bugs are introduced is eye-opening. For more mature teams, it's about improving test reliability or cutting flakiness in CI. What are your go-to quality metrics? #QualityEngineering #SoftwareTesting #TestAutomation #QACommunity #EngineeringExcellence #DevOps #TestingMetrics #FlakyTests #ProductQuality #TechLeadership #ShiftLeft #ShiftRight #QualityMatters

I see multiple threads in various forums where some of you are very skeptical about measuring productivity, effectiveness, and efficiency metrics of testers. Some other proclaimed gurus and testing experts are completely against metrics. In order to form an opinion on metrics one should first try to understand what benefits one can get from metrics and KPIs and why do we need to measure anyone’s productivity. The big misconception is that productivity metrics are not fair as they can paint a completely inaccurate picture and are used only as a tool to identify poor performance or used against employees when it comes to firing decisions or salary revisions and appraisals. I would agree that improper collection of metrics can lead to inaccurate outcomes and as a result, unfair decisions. Therefore, instead of refusing and blaming metrics for usefulness and unfairness one should understand how to collect metrics the proper way and how to make metrics collection and calculation a fair process which will provide benefits to all. One thing I realized throughout my 27 years of career in Software Quality Assurance and Testing individuals who do not like metrics or biggest opposers to metrics simply afraid of them. They are afraid to measure their own productivity within organization and value add. They are afraid to be revealed that they are not effective because they only can talk fluff but cannot execute or take actions. Each employee should bring ROI to the organization they work for. How is this ROI measured? Yes, you guessed it right, it is measured through productivity and effectiveness. Below are my favorite metrics which help me to understand better on what throughput we as organization can achieve for our clients and customers, provide better insights on test estimation, timelines, capabilities, future projections and value add from each QA Professional. Test Effectiveness of a Tester: ✅ Percentage (%) of Defect Leakage against assigned User Story ✅ Percentage (%) of Defect Leakage against assigned Functionality ✅ Percentage (%) of Defect Leakage against assigned Module ✅ Percentage (%) of Defect Leakage against assigned Regression Testing Cycle ✅ Percentage (%) of Schedule Variance for a Tester ✅ Percentage (%) of Effort Variance for a Tester ✅ Percentage (%) of Defect Rejection Rate ✅ Number of Suggestions/Recommendations/Improvements raised by a Tester ✅ Percentage (%) of Suggestions/Recommendations/Improvements raised by a Tester ✅ Number of Critical Defects identified per Sprint/Release/Week/Month/Year ✅ Number of Static Testing defects identified per Sprint/Release/Week/Month/Year Watch this video on how to measure TEI (Test Efficiency Indicator) and Defect Leakage the proper way -> https://lnkd.in/gnz6SGWw Your comments, feedback, concerns are always welcomed.