Choosing the right test metrics and product quality

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What to Measure Using Test Metrics?

Metrics shape business development. Software testing companies must track the right indicators to measure progress and outcomes. Yet metrics valued by teams often fail to align with broader business objectives. As Melanie Ziegler, founder of VPE Forum, observed, revenue comes from results, not activity. Software testing is no exception: the metrics chosen directly influence how organizations perceive and evaluate quality assurance.

What Are Testing Metrics?

Testing metrics are quantitative indicators used to assess the progress, quality, performance, and effectiveness of the testing process. By analyzing these figures, teams can refine workflows, boost efficiency, and make data-driven decisions that optimize delivery.

Just as a gram measures weight, software teams rely on their own benchmarks—for example, the number of defects per 1,000 lines of code. Measurable results help organizations link quality improvements to return on investment. As the saying goes, “You can’t manage what you can’t measure.”

HelloFresh illustrates this principle. Initially focused on subscriber growth, the company later examined how quality issues affected cancellations. Redirecting testing efforts toward the areas that influenced this metric helped reduce critical defects in releases.

Numerous metrics exist. Some track SDLC success, others evaluate product quality or team efficiency. Calculations follow established formulas. For instance, test-case completion is measured by dividing executed test cases by the total number written, then multiplying by 100.

Challenges with Metrics

Even with clear formulas, selecting the right metrics remains difficult. Testing aims to reduce risk and increase transparency. Poorly chosen indicators, however, can distort priorities. A team rewarded solely for finding the highest number of bugs may overlook high-value functionality, creating an illusion of quality.

Numbers alone rarely tell the full story. As economist Charles Goodhart noted, “When a measure becomes a target, it ceases to be a good measure.” Teams providing software testing services must therefore prioritize indicators that deliver genuine business value rather than vanity metrics.

Sound logic should guide every measurement. If an automated test suite runs longer than two hours, splitting it into separate pipelines—running critical tests first, followed by regression—often improves efficiency.

Key Testing Metrics Worth Tracking

User Satisfaction

Customer behavior directly reflects product quality. During quality assurance audits, teams should start with user reactions to the application and its error messages. Research shows that 92% of users abandon an app when quality falls short. Dissatisfied customers also spread negative experiences—nearly 13% share complaints with more than 20 people—damaging brand reputation.

Process Metrics

Process metrics reveal how efficiently work moves from requirement to production. Common measures include lead time from requirement creation to deployment, time spent developing a feature after approval, and mean time to resolution. Identifying bottlenecks helps teams streamline delivery.

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Coverage Indicators

Test coverage shows which parts of the code or requirements have been exercised. A recommended top-down approach begins with module and feature coverage before drilling into data-level details. Studies indicate that coverage levels between 70% and 90% correlate with reliable software while reducing ambiguity across large teams.

Code Quality Metrics

Automated analysis with tools such as SonarQube helps quantify technical debt and surface vulnerabilities. High-quality code is characterized by reliability, maintainability, testability, and portability. Five core indicators—maintainability index, complexity metrics, depth of inheritance, class coupling, and lines of code—provide a practical framework for assessment.

Error and Incident Indicators

Tracking crashes and downtime offers direct insight into end-user experience. When logging issues, teams should distinguish critical defects from enhancement suggestions. For certain business models, such as e-commerce platforms with seasonal traffic spikes, performance under load may outweigh other quality dimensions.

Exploratory Testing Metrics

Exploratory testing examines what an application can and cannot do without predefined scripts. Michael Bolton and James Bach proposed two practical approaches: recording setup, execution time, and defects per session, or comparing time spent on charter review versus observing interesting product behavior. Additional useful indicators include exploratory versus scripted hours, average session duration, session breakdown, number of sessions, and defects per session.

Test Automation Metrics

As codebases grow, test coverage can decline while delivery costs and time increase. Automation metrics provide visibility into whether automation initiatives are reversing these trends by improving coverage and accelerating releases.

Performance Indicators

Understanding behavior under peak load helps prevent post-release failures. Load testing typically measures response time, error rate, throughput, and system bottlenecks.

Team “Happiness” Indicators

These metrics assess team sustainability and workload balance. They help answer questions such as whether tasks can be covered during absences or whether burnout risks exist. Although harder to quantify, tracking team well-being supports consistent quality and productivity.

Conclusion

Intense market competition demands shorter release cycles. QA teams must therefore accelerate alongside development while maintaining quality. The right metrics supply the visibility needed to achieve this balance. Metrics, however, require regular review; as projects evolve in 2026 and beyond, indicators should be updated to reflect new priorities and technologies. Ultimately, the objective remains unchanged: delivering software that provides real value to users.

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