Testing Metrics: Measure your Testing Success (Part 3)

Measuring Test Execution for Effective Quality Delivery

The Test Execution phase is often perceived as a checklist-driven activity, with teams focused on running test cases, logging defects, and meeting release deadlines. In the pursuit of timely delivery, evaluating test effectiveness for ensuring quality, monitoring progress for schedule adherence, and assessing process efficiency for improvement are frequently overlooked. While testing status gets marked as complete with test execution phase getting over, one cannot report about coverage gaps, delays, defect trends and process improvisations, etc.

Today, when the pace of development is increasing and testing is continuous activity, it becomes essential to objectively identify and analyse using well-defined metrics to reduce re-work, avoid compromising on quality to help increase stakeholder confidence.

Let’s explore in this blog, the metrics that can be captured to offer valuable insights during the Test Execution phase.

Note: You can also refer to our earlier blog in this series, which highlights the essential metrics for the Requirements Analysis (Testing Metrics: Measure your Testing Success (Part 1)) and Test Planning (Testing Metrics: Measure your Testing Success (Part 2)) phases.

1. Test Execution Coverage

This measures the extent to which planned test cases have been executed within a given test cycle. It helps assess overall progress and ensures that testing aligns with schedule and scope.

Formula:

Test Execution Coverage (%) =
	(Number of executed test cases/Total planned test cases) × 100

Target: ≥ 95%, depending on project criticality and schedule constraints.

2. Defect Density

This metric indicates the number of confirmed defects identified in a component or system relative to its size (e.g., lines of code, functional area, or modules). It reflects product quality and testing effectiveness.

Formula:

Defect Density =
	No. of confirmed defects/Size of the software (e.g., KLOC or module)

Target: ≤ As per benchmark based on historical project data and product complexity.

3. Defect Severity Distribution

This classifies defects based on their impact on system functionality. Tracking severity distribution helps prioritize fixes and gauge overall product stability.

Formula:

Defect Severity (%) =
	(No. of defects of a specific severity/Total no. of defects) × 100

Target: High-severity defects ≤ 10% of total defects before release.

4. Mean Time To Detect (MTTD)

This measures the average time taken to identify a defect after its introduction during test execution. It indicates the responsiveness and efficiency of the testing process.

Formula:

MTTD = Total time taken to detect all defects/Total defects detected

Target: Within 1–2 days of defect introduction during active testing.

5. Mean Time To Resolution (MTTR)

This represents the average time required to fix a defect after it’s reported. It evaluates the efficiency of defect management and developer–tester collaboration.

Formula:

MTTR = Total time taken to resolve all defects/Total resolved defects

Target: Within 3–5 days depending on defect severity and project size.

Sprint Metrics Matrix

Let’s look at the matrix below to track performance, spot risks, and make informed decisions during the test execution phase.

Sprint	Test Execution Coverage	Defect Density (per 30 modules)	Defect Severity (High)	Mean Time to Detect (MTTD)	Mean Time to Resolution (MTTR)	Notes / Actionable Insights
8	60%	12	15%	2.5 days	7 days	Execution lagging, high severity defects – focus on coverage gaps and faster detection
9	80%	8	10%	2 days	5 days	Improved but still high severity, focus on test coverage gaps and defect triage efficiency.
10	95%	4	5%	1.5 days	3 days	On track: Implement root-cause analysis for remaining defects
11	98%	2	3%	1 day	2 days	Prepare for release exit criteria and monitoring

Final Thoughts

By tracking the right metrics during Test Execution, teams can move beyond simply running tests to truly managing quality. These insights help identify process inefficiencies, control defect trends, and ensure that testing remains aligned with project goals.

In our next blog, we'll be exploring key metrics that help evaluate testing effectiveness during the Test Closure phase.

References

1. Testing Metrics - Are They Really So Important?

2. QA Metrics - TestRail

3. Software Testing Metrics, its Types and Example - GeeksforGeeks

4. Essential Metrics for the QA Process | BrowserStack