Software Testing Metrics – Why it Matters, Types & Example

Last Updated: August 21, 2025

In the current scenario, quality control is the luring force behind the success and popularity of software products, which has drastically amplified the requisite of taking efficient measures for quality. But how do you measure the efforts of the software testers and the software testing process? Software testing metrics can do that for you.

That is why software testers use different metrics for testing and Key Performance Indicators (KPIs) to gauge their aims and efficacy throughout the STLC.

What Are Software Testing Metrics?

Software testing metrics are quantitative measures that evaluate the test efficiency and effectiveness of the overall testing process. These metrics help analyze and monitor test activities such as the testing procedure, quality, productivity, progress, and overall health.

The goal is to boost the effectiveness and conclusiveness of the software testing process and support the fabrication of better resolutions for future testing by delivering precise data about the test proceedings.

A metric expresses the grade to which a system or a process possesses a given criterion in numerical stints.

Why Are Software Testing Metrics Important?

Test metrics are essential for determining how well an application works and performs. Developers and testers can use them to identify testing bottlenecks and make the process more efficient. They also show what changes need to be made so that the application is perfect and of good quality.

These metrics provide insights into various aspects of the testing lifecycle, such as test coverage, defect detection, and overall quality. You can improve your software testing process only when you measure it with the appropriate software testing metrics.

By measuring these, you can

Evaluate the time and cost spent on testing
Understand the risks associated with the product
Improve the efficiency of your automated testing
Reduce the costs associated with bug-fixing
Improve your project planning process
Promote continuous improvement

Significance of Metrics in Testing

Metrics decide the software’s quality and performance. Developers may utilize the appropriate software testing standards to enrich their productivity. A few critical software testing standards are given below:

Testing benchmarks assist in deciding what kinds of refinements are needed to generate a high-quality, flawless software product.
Making reasonable jurisdictions regarding the various testing facets that succeed, like project scheduling, design plan, and expense estimations.
Examining the prevailing technology or operation to determine if it demands further changes.

Types of Software Testing Metrics

Software testing metrics are split into three groups.

Process Metrics: The process metrics outline the characteristics and performance of a design. These features add to the SDLC (Software Development Life Cycle) procedure’s enhancement and conservation.
Product Metrics: A product’s design, size, quality, performance, and complexity are delineated by the product metrics. Developers can amend the caliber of their software development by employing these features.
Project Metrics: Project metrics are employed to evaluate the generic quality of a project. It estimates the design resources and deliverables and decides productivity, cost, and possible flaws.

It is crucial to ascertain the befitting testing measures for the operation. Some of the points to keep in mind are the following:

Choosing the target audiences precisely before creating the metrics.
Outlining the objective for which the standards were developed.
Formulating measures based on the project-specific necessities.
Estimating the financial gain chummed with every statistic.
Matching the measurements to the design life cycle for achieving the best results.

A substantial advantage of automated testing is that it allows testers to finalize more tests in less time while covering a large composition of variations that would be practically tough to compute manually.

You can read more about metrics in SDLC here: Metrics in SDLC: Let the Truth Prevail

Test Metrics Life Cycle

The Test Metrics Life Cycle is gathering information, looking at it, and reporting on it to determine how successful a software project is. It starts by picking the right metrics that will show progress and what needs to be fixed. Then you collect data from logs, bug-tracking systems, and performance tests. After that, you review all the information you gathered and report on it to see how well the software works. Finally, with this knowledge, changes can be made to make the product or process better. By tracking test metrics throughout a project’s life cycle, companies can ensure their work is getting them closer to their goal.

Analysis:

Recognizing the most appropriate metrics for testing.
Defining the adopted QA standards.

Communicate:

Training the software testing team on the data points to be collected for processing the recognized metrics.
Informing the testing team and the stakeholders of the requirements.

Evaluation:

Capturing and then verifying the data.
Using the collected data for evaluating the value of the metric.

Report:

Creating a sound and compelling inference for the paper.
Gathering potent inputs from the stakeholders and representatives based on the information. Distribution of these reports to the representatives and stakeholders.

Calculating Software Test Metrics

Calculating testing metrics involves using specific formulas to quantify various aspects of the testing process. Here are some crucial testing metrics and how to calculate them:

Formula for Test Metrics

You can track the quality and progress of the testing process using specific formulas for each test metric. Below are some of the most used test metrics, along with their formulas,

1. Test Cases Executed Percentage: It measures the status of the test cases executed for the entire application.

Percentage of test cases executed = (No of test cases executed / Total no of test cases written) x 100

2. Defect Density: It measures the number of defects found per line of code or module and evaluates the quality of the code. The higher the software quality, the lower the defect density.

Defect Density = Total Defects / Size of the Module

3. Test Case Effectiveness: It measures the efficiency of test cases in identifying defects.

Test Case Effectiveness = (Defects Detected / Test Cases Run) x 100

4. Defect Leakage: It evaluates the percentage of defects that are left uncovered by the testing team but found by end users or customers.

Defect Leakage = (Defects Found Post-Testing / Defects Found Pre-Testing) x 100

5. Defect Removal Efficiency: It evaluates the effectiveness of the testing process in identifying defects throughout the SDLC.

Defect Removal Efficiency = (Defects Removed / Total Defects at Start) x 100

6. Test Coverage: It measures the extent to which the application’s source code, functionalities, or requirements are tested.

Test Coverage = (Tested Functionalities / Total Functionalities) x 100

7. Passed Test Cases Percentage: It evaluates the percentage of the test cases that passed.

Passed Test Cases = (Passed Tests / Total Tests Executed) x 100

8. Failed Test Cases Percentage: It evaluates the percentage of the test cases that passed.

Failed Test Cases = (Failed Tests / Total Tests Executed) x 100

9. Blocked Test Cases Percentage: It assesses the percentage of test cases that are blocked.

Blocked Test Cases Percentage = (Total number of blocked tests / Total number of tests executed) x 100

10. Fixed Defects Percentage: It measures the percentage of defects that are fixed.

Fixed Defects Percentage = (Total number of defects fixed / Number of defects reported) x 100

11. Test Execution Rate: It evaluates the overall testing speed.

Test Execution Rate = (Test Cases Executed / Planned Test Cases) x 100

12. Accepted Defects Percentage: It measures the percentage of defects that are accepted by the development team out of the total number of defects reported.

Accepted Defects Percentage = (Defects Accepted as Valid by Developers/ Total Defects Reported) x 100

13. Defects Deferred Percentage: It measures the percentage of defects that are deferred for future releases.

Defects Deferred Percentage = (Defects deferred for future releases / Total Defects Reported) x 100

14. Defect Age: It evaluates the time it takes to fix a defect.

Defect Age = Time of Defect Fix – Time of Defect Discovery

15. Rework Effort Ratio: It measures the effort required to rework or correct defects.

Rework Effort Ratio = (Rework Effort / Total Testing Effort) x 100

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Examples of Software Test Metrics Calculation

Let’s consider a scenario to calculate the above-discussed metrics for testing,

Software Test Metrics	Data From Test Case Development
Number of requirements	10
The average test cases per requirement	5
Total test cases created	100
Total test cases executed	80
Passed test cases	50
Failed test cases	20
Blocked test cases	0
Unexecuted test cases	10
Total defects identified	10
Valid defects accepted by the team	10
Deferred defects	0
Fixed defects	10

Now, let’s calculate the metrics for testing using the formulas we discussed above,

Percentage Test Cases Executed = (80 / 100) x 100 = 80
Test Case Effectiveness = (10 / 80) x 100 = 12.5
Passed Test Cases Percentage = (50 / 80) x 100 = 62.5
Failed Test Cases Percentage = (20 / 80) * 100 = 25
Fixed Defects = (10 / 10) x 100 = 100

Other Test Metrics in Software Testing

Some of the other test metrics in software testing, apart from the ones mentioned above,

Test Effort

Testing effort standards will answer the question, “how long or how many or how much?” They are practiced to establish baselines for test planning. However, these metrics are mean values where 50% of the values fall over the mean and 50% under.

Some of these specific metrics are:

Tests run per period = Total number of tests run / Total time taken
Test design efficiency = Total number of tests designed / Total time taken
Test review efficiency = Total number of tests reviewed / Total time taken
Defects per test hour = Total number of defects / Total number of test hours
Bugs per test = Total number of bugs found / Total number of tests
Time to test a bug = Total time taken between defect fix to retest for all defects / Total number of bugs found

Test Effectiveness

Test effectiveness finds a solution to “how good are the tests?” It evaluates the bug-finding quality and ability of a test set. Test effectiveness measures generally express the difference between the total number of defects reported by the QA team and the overall defects found in terms of percentage.

Test effectiveness using defect containment efficiency: The higher the test effectiveness, the better the test set and the lesser the long-term maintenance effort will be. For instance, if the test effectiveness is 70%, it concludes that 20% of the defects are removed from the testing operation.
Context-based test effectiveness using team assessment: Defect containment efficiency metrics do not come in handy in the following cases:

Already mature product
Buggy and unstable product
Lacking enough tests due to constraints of time or resource

In such cases, another way to estimate test set effectiveness is using a context-based approach.

For instance, in a particular context, the QA team decides that a befitting test set needs to cover high-risk demands adequately.

Test Coverage

Software quality metrics estimate the fitness of the application under the testing process. The following core block of standards that need to be analyzed revolves around the testing coverage. Test coverage benchmarks gauge the test exertion and help determine the operations’ significance.

Given below are some crucial test coverage benchmarks.

Test Execution Coverage

This provides a model of the comprehensive tests administered compared to the unsettled test runs. It is typically expressed as a percentage.

Test Requirements Coverage

The number of demands covered by the total amount of scoped needs for a release, design, sprint, or project must be divided and analyzed to achieve a high-level prospect of the requisites having test coverage. Using standardized approaches and clear documentation like how an AI flyer maker provides will ensure that requirements are organized, traceable, and easy to validate throughout the testing process.

Read her more on Test Coverage Metrics

Test Economics Metrics

Infrastructure and tools contribute to the expense of testing. Testing systems do not have fathomless financial resources to spend. Thus, estimating how much you can spend and how much you indeed wrap up spending is eventful.

Here are a few test economics measures that can provide insight into budget planning:

Total allocated costs for testing

It refers to the amount that QA directors and CIOs have calculated for all testing exercises and resources for single dev projects for the entire year.

The actual cost of testing

It refers to the real money that went into the testing operation.

It is assumed that all testing sets are equal in complexity. For illustration, if the budget is $1000 and includes testing 100 necessities, the cost of trying a requisite is $10. These values are substantial as they help estimate future projects and systems budgets.

Budget variance

The variance between the actual and planned costs is referred to as budget variance.

Schedule variance

It is the difference between the actual time taken to complete tests and the planned time.

Cost per bug fix

It refers to the amount of effort spent on a defect per developer.

However, the cost of a bug fix is 10 * $60 = $600; if a developer spends 10 hours fixing a defect, their hourly rate is $60.

Cost of not testing

All the finances that went towards the rework equate to the cost of not testing If a block of new features went into production but claimed rework. The expense of not testing can also be silhouetted to a more subjective value, similar to a person’s perspective.

Some examples of a subjective cost of not testing are as follows.

More client care calls and service requests.
Productive outages
Loss of user/ client trust
Loss of client fidelity
Poor brand awareness

Test Team Metrics

These can be exploited to deduce if work allotment is uniform for each test squad member and to check if anyone needs added process/ project knowledge expositions. These criteria should never be used as an erudition to attribute fault.

Distribution of defects returned per team member
Distribution of open defects for retest per test team member
Test cases allotted per test team member
Test cases executed by test team member

Usually, histograms or pie charts are created to get a quick snap of the job assignment. It allows the testing manager to determine the cause and take remedial actions if demanded.

Test Execution Status

The test execution snap chart shows the complete implementations disposed of as passed, failed, blocked, incomplete, and unexecuted for easy engrossment of the test sprint status.

These charts are great optical apprentices for the daily status huddle because raw figures have a high chance of sagging through people’s brains. The rising and contracting bars captivate attention and impart advancement and speed much more effectively.

Status Chart
Defect Find Rate Tracking
Tracking and Defect Find Rate Tracking

The theoretical curve is plotted using these cumulative test execution rates and defect counts. Compared to the raw figures, these charts shall signal early that the testing course needs to be changed if the targets need to be reached.

Effectiveness of Change Metrics

Software undergoes a handful of frequent changes. Changes typically invoke new deformities, catalyze timelines to sag, reduce operation robustness, and endanger quality. Embodied modifications must be watched precisely to conclude their concussion on the robustness and stability of the existing product.

The following benchmarks can help in a better understanding of concussions.

Effect of Testing Changes

It numerically refers to the total number of defects that can be put down to changes. This could denote ensuring defects have been affected and fixing visions attached when they are reported to the development team.

Manual Testing Metrics: What Are They and How Do They Work?

It is done in a step-by-step manner by the QA time without using any test automation tools, frameworks, or libraries. This is time-consuming and repetitive, yet can be useful in certain scenarios that require manual intervention. The two types of testing metrics are as follows,

Base Metrics

Fundamental QA metrics, also known as base metrics, are a composition of absolute numbers collected by analysts throughout the development and execution process. Some of them are :

Number of test cases
Number of passed, failed, and blocked test cases
Total number of defects and critical issues reported, accepted, rejected, and deferred
Number of planned and actual test hours
Number of bugs discovered after shipping

Derived Metrics

Base metrics are the fundamental starting point, but only placing those values is not enough. Testers should also derive some useful benchmarks using mathematical computations.

Only tabulating the absolute numbers collected by analysts and testers produces more confusion than solutions. Hence, we can dive deeper into solving the glitches and flaws in our software testing course using the derivative metrics.

Read here – Guide on Manual Testing

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Final Thoughts

Software testing metrics and crucial performance indexes enrich the course of software testing. From securing the precision of the multiple tests carried out by the testers to authenticating the class of the product, these benchmarks play a pivotal purpose in the software development lifecycle.

Hence, by enforcing and executing these testing standards and performance pointers, the effectiveness and accuracy of the testing efforts can be exponentially increased to get a phenomenal grade for software products.

Frequently Asked Questions:

What Are Metrics in QA?

Metrics in QA s are measurements that software developers use to ensure their products are up to scratch. They involve testing the product to check for any possible problems or issues. These tests help identify any weaknesses in the product so they can be fixed before it’s released.

What Are Productivity Metrics in Software Testing?

Software testing productivity measures the test efficiency and test effectiveness. It provides insights into resource utilization, test execution time, and other factors that increase productivity and testing efficiency.