Software Testing Metrics Guide; Definition, Types & Example

The ultimate guide to software testing metrics: definition, types, and examples explained for informed decisions. Enhance your testing process now!

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Software testing metrics are critical in assessing the progress, quality, productivity, and general health of the software testing process. These quantitative metrics attempt to improve the efficiency and efficacy of testing activities. A metric, in essence, uses numerical values to quantify the extent to which a system, component, or process exhibits a given characteristic.

To illustrate this concept, let's consider the analogy of a car's weekly mileage compared to the ideal mileage specified by the manufacturer. Software testing metrics, just like this comparison, provide essential information about the testing process. They allow for the evaluation of testing effectiveness, the identification of prospective areas for improvement, and the measuring of conformance with set quality standards.

What Is Software Testing Metrics?

Software testing metrics are employed in software development to oversee and assess the process of testing the software. These indicators furnish valuable insights into the quality, progress, and areas necessitating enhancement across different testing phases. By vigilantly monitoring key performance indicators (KPIs), teams can identify issues within the ongoing cycle and introduce the requisite modifications for subsequent iterations.

The primary purpose of metrics is to provide a means of comparative measurement for the testing procedure. By comparing current metrics to historical data or industry benchmarks, teams can gauge their performance and identify opportunities for improvement. This data-driven approach empowers them to make informed decisions that enhance the efficacy and efficiency of the testing process, ensuring it remains effective in practice.

Importance of Metrics in Software Testing

Importance of Key Performance Indicators in software testing

By Sathwik Prabhu

Software testing metrics are useful tools in the software development lifecycle, providing several benefits that help produce positive results. Let's examine these indicators' criticality in more detail:

  • Defining Testing Goals and Measuring Results
  • Software testing metrics make it possible to set precise testing goals and quantify results. Teams can monitor their performance and spot areas that need development since they have a formal framework for defining goals and tracking progress.

  • Understanding Risks Associated with Software
  • Teams can acquire insights into the potential risks and vulnerabilities in the program by evaluating and monitoring several indicators, such as defect density, failure rates, and vulnerability assessments. With this knowledge, they are better equipped to prioritize their testing efforts, manage resources effectively, and deal with important issues so that risks can be properly mitigated.

  • Improving Automated Testing
  • Software testing metrics are essential to increasing the efficiency of automated testing. Teams can pinpoint areas for automated testing process optimization and refinement by monitoring metrics like test coverage, test execution time, and test suite effectiveness.

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  • Enhancing Project Planning Processes
  • Software testing metrics aid in project planning improvement. Teams can make data-driven decisions on resource allocation, timeframes, and testing strategies by examining previous data and trends.

  • Minimizing Costs Associated with Defects
  • The ability to reduce costs related to faults is one of the key importance of employing software testing metrics. Teams may drastically reduce costs associated with rework, customer unhappiness, and potential harm to the brand name by recognizing and addressing issues early in the development cycle. Early fault detection and mitigation improve overall cost effectiveness while also saving time and labor.

  • Optimizing Business Processes in Product Development
  • Software testing metrics can help with the optimization of a number of business processes that are involved in product development. Organizations are able to spot bottlenecks, reorganize workflows, and take well-informed decisions to increase overall efficiency by examining metrics related to defect detection, test cycle time, and test effectiveness.


Types of Software Testing Metrics

There are three types of software testing metrics:


1. Process Metrics

Process metrics encompass measurable indicators that act as reference points for assessing the effectiveness of a specific procedure. These indicators consist of elements such as the pace at which activities are executed, the duration required to finalize particular stages, the degree of resource utilization, and other pertinent factors.

Through careful examination of these indicators, supervisors can acquire valuable insights into the efficiency of the process, enabling them to make informed choices aimed at enhancing its overall performance. This, in turn, facilitates the development of fresh guidelines and strategies designed to optimize the different phases of the procedure.

2. Product Metrics

Product metrics refer to measurements utilized to assess the excellence, magnitude, effectiveness, and productivity of a specific product. These measurements offer significant insights into the qualities and features of the product, allowing interested parties to appraise its comprehensive worth and functionality.

Through the utilization of these metrics, companies can evaluate variables such as customer contentment, fault frequencies, product proportions, and other relevant elements. This assessment aids in comprehending the product's merits and drawbacks, thus supporting enhancements and elevating its general caliber.

3. Project Metrics

Project metrics encompass a diverse range of measurements utilized to assess the efficiency and excellence of a project. These metrics consider aspects such as resource utilization, project duration, project expenses, and other temporal considerations.

By analyzing these indicators, project managers can monitor the project's progress, identify any impediments, and implement necessary measures to ensure its success. These metrics provide valuable data on the efficacy and proficiency of resource allocation, time management, and cost control, enabling improved project outcomes and informed decision-making.

16 Fundamental Software Testing Metrics

Software testing metrics are important markers that offer perceptions of the caliber and efficiency of the testing process. They aid in measuring numerous testing parameters, spotting potential problems, and monitoring development. These basic software testing metrics are provided:

  • Derivative Metrics:
  • Derivative metrics help identify specific areas within the software testing process that require attention, enabling teams to take effective steps to improve accuracy and effectiveness.

  • Defect Density:
  • Defect density is a fundamental metric that measures the total number of defects discovered in software during a specified period, divided by the size of the corresponding module. This metric helps determine whether the software is ready for release or requires further testing. Defect density is typically measured per thousand lines of code (KLOC).

    Formula: Defect Density = Defect Count / Size of the Release/Module

  • Defect Leakage:
  • Defect leakage measures the effectiveness of the testing process before user acceptance testing (UAT). It quantifies the number of defects left undetected by the testing team, which are subsequently discovered by end-users. This metric provides insights into the overall quality of the testing process.

    Formula: Defect Leakage = (Total Number of Defects Found in UAT / Total Number of Defects Found Before UAT) x 100

  • Defect Removal Efficiency (DRE):
  • Defect removal efficiency evaluates the development team's ability to identify and address defects before software release or implementation. It measures the effectiveness of defect removal methods used during the testing phases.

    Formula: DRE = Number of Defects Resolved by the Development Team / (Total Number of Defects at the Moment of Measurement)

  • Defect Category:
  • Defect category metrics rate the software's usability, performance, functionality, stability, and dependability along with other quality-related factors. These measurements shed light on how flaws are distributed among different quality criteria.

    Formula: Defect Category = Defects Belonging to a Specific Category / Total Number of Defects

  • Defect Severity Index (DSI):
  • The Defect Severity Index (DSI) assesses the impact that flaws have on the creation of software or on certain components. It aids in evaluating the software's quality and the efficiency of the testing team's work.

    Formula: DSI = (Sum of (Defect * Severity Level)) / Total Number of Defects

  • Review Efficiency:
  • Review efficiency aims to reduce pre-delivery defects by identifying and resolving errors during the review process. It helps decrease the probability of defect leakage in subsequent testing stages and validates the effectiveness of test cases.

    Formula: Review Efficiency (RE) = Total Number of Review Defects / (Total Number of Review Defects + Total Number of Testing Defects) x 100

  • Test Case Effectiveness:
  • Test case effectiveness measures the efficiency of test cases executed by the testing team during each testing phase. It helps determine the quality of test cases and their ability to detect defects.

    Formula: Test Case Effectiveness = (Number of Defects Detected / Number of Test Cases Run) x 100

  • Test Case Productivity:
  • Test case productivity measures the number of test cases prepared by the testing team and the effort invested in the process. It provides insights into the test case design productivity and serves as input for future estimation.

    Formula: Test Case Productivity = (Number of Test Cases / Effort Spent for Test Case Preparation)

  • Test Coverage:
  • Test coverage assesses the extent to which the software's complete functionality is covered by testing. It indicates the completion of testing activities and can be used as a criterion for concluding testing.

    Formula 1: Test Coverage = Number of Detected Faults / Number of Predicted Defects

    Formula 2: Requirement Coverage = (Number of Requirements Covered / Total Number of Requirements) x 100

  • Test Design Coverage:
  • Test design coverage measures the percentage of test cases that cover the requirements of the software. It helps evaluate the functional coverage of the test cases and improves overall test coverage.

    Formula : Test Design Coverage = (Total Number of Requirements Mapped to Test Cases / Total Number of Requirements) x 100

  • Test Execution Coverage:
  • Test execution coverage provides insights into the progress of testing by indicating the number of test cases executed and the number of pending test cases. This metric helps determine the overall coverage of testing activities.

    Formula : Test Execution Coverage = (Total Number of Executed Test Cases or Scripts / Total Number of Test Cases or Scripts Planned to be Executed) x 100

  • Test Tracking & Efficiency:
  • Test tracking and efficiency metrics help evaluate the efficiency of testing activities and provide insights into the defects and progress of the testing process.

    Metrics include:

    • Passed Test Cases Coverage: Measures the percentage of passed test cases.
    • Formula: (Number of Passed Tests / Total Number of Tests Executed) x 100

    • Failed Test Case Coverage: Measures the percentage of failed test cases.
    • Formula: (Number of Failed Tests / Total Number of Test Cases Failed) x 100

    • Test Cases Blocked: Determines the percentage of test cases blocked during the testing process.
    • Formula: (Number of Blocked Tests / Total Number of Tests Executed) x 100

    • Fixed Defects Percentage: Identifies the percentage of defects that have been fixed.
    • Formula: (Number of Defects Fixed / Total Number of Defects Reported) x 100

    • Accepted Defects Percentage: Measures the percentage of defects accepted by the development team.
    • Formula: (Number of Defects Accepted as Valid / Total Number of Defects Reported) x 100

    • Defects Rejected Percentage: Evaluate the percentage of defects rejected by the development team.
    • Formula: (Number of Defects Rejected by the Development Team / Total Number of Defects Reported) x 100

    • Defects Deferred Percentage: Determines the percentage of defects deferred for future releases.
    • Formula: (Number of Defects Deferred for Future Releases / Total Number of Defects Reported) x 100

    • Critical Defects Percentage: Measures the percentage of critical defects in the software.
    • Formula: (Number of Critical Defects / Total Number of Defects Reported) x 100

    • Average Time Taken to Rectify Defects: This metric calculates the average time taken by the development and testing team to rectify defects, providing insights into the efficiency of defect resolution.
    • Formula: (Total Time between Defect Fix & Retest for All Defects) / (Total Number of Defects)

  • Test Effort Percentage:
  • Test effort percentage evaluates the estimated effort for testing compared to the actual effort invested. It helps assess the accuracy of effort estimation for future projects.

    Metrics include:

    • Number of Test Runs per Time Period: Measures the number of tests executed within a specific time frame.
    • Formula: (Number of Test Runs / Total Time)

    • Test Design Efficiency: Evaluate the efficiency of test design by measuring the number of tests executed over a specific time period.
    • Formula: (Number of Test Runs / Total Time)

    • Bug Find Rate: Measures the number of defects found during testing per unit of time.
    • Formula: (Total Number of Defects / Total Number of Test Hours)

    • Number of Bugs Per Test: Determines the number of defects found during each testing stage.
    • Formula: (Total Number of Defects / Total Number of Tests)

  • Test Effectiveness:
  • Test effectiveness measures the ability of testing to find and isolate defects in the software. It provides insights into the quality of the test set and helps identify the percentage of defects found by the testing team compared to the total number of defects in the software.

    Formula : Test Effectiveness (TEF) = (Total Number of Defects Injected + Total Number of Defects Found) / Total Number of Defects Escaped ) x 100

  • Test Economic Metrics:
  • Test economic metrics focus on evaluating the cost of testing activities and comparing the estimated costs with the actual expenditure during the testing process. These metrics provide insights into the financial aspects of testing.

    Metrics include:

    • Total allocated cost of testing.
    • Actual cost of testing.
    • Variance from the estimated budget.
    • These metrics help assess the financial efficiency of the testing process and aid in estimating future projects accurately.

How to Calculate Test Metrics

Calculating test metrics correctly is essential for assessing the efficacy and efficiency of testing processes. The techniques listed below will help you compute and analyze these metrics carefully so you can find areas for improvement and streamline your testing procedure.

Step 1: Finding Processes to Measure is the first step. A thorough test plan that adheres to the application requirements must be created first. The manual test-case scenarios in this plan should cover a range of application-related topics, including specific components, business requirements, user behavior, performance, and functionality. List the testing techniques you want to keep an eye on and improve. For instance:

Example: a project manager assigns the QA team to find and fix defects in an application. The project manager also seeks to keep tabs on the cost of the software testing.

Step 2: Once the testing metrics have been determined, it is essential to discuss the specifics with management and other stakeholders to get their consent. Create baseline values for each statistic as well. These baselines act as a point of comparison for assessing the effectiveness of testing processes throughout each iteration. Continuing with the prior illustration:

Example: The QA team works hard to keep track of the timeline, the number of test cases performed, the issues encountered, the cost to fix each bug, and the overall cost of the testing process. The testing budget will be capped by the project manager at Rs. 3 lakhs, which serves as the testing metric's baseline.

Step 3: After creating the baseline, conduct extensive testing to identify application issues and carry out the necessary fixes. Observe and review the crucial information you need to keep track of. The analysis of testing metrics requires the usage of a huge dataset.

Example: the QA team scrupulously documents each step of the testing process and calculates that it will cost about Rs. 5 lakhs in total. The actual cost of this testing method reveals a 2 lakh budget discrepancy when the predicted cost is subtracted from the actual value.

Step 4: Finding Improvement Opportunities; To identify areas that can be optimized, compare the actual values obtained with the baseline values. In each cycle, carry out these actions again while meticulously recording the outcomes. Create a thorough report on the metric that includes its formula, experimental strategy, and results.

For instance, some tactics to reduce the price of software testing include:

  • Removing unnecessary test cases.
  • Incorporating testing into the design and development of new products.
  • Testing automation using testing tools.
  • Comparing benchmarks to industry standards.

This methodical methodology will help you compute and analyze test metrics efficiently. With the help of these data, you can see how effective your testing procedure is and pinpoint areas that need work. As previously indicated, implementing optimization tactics can further improve the overall efficacy of software testing.

Test Metrics Life Cycle

The Test Metrics Life Cycle is a crucial component of software project evaluation. To evaluate the project's progress, data must be gathered, evaluated, and reported. This cycle tries to pinpoint potential areas for future growth and pick measurements that fairly depict progress. Data is gathered from a variety of sources, such as logs, bug-tracking software, and performance testing.

After the obtained data has been appropriately analyzed, a report is then produced to evaluate the software's performance. With this knowledge, it is possible to enhance the process, the item, or the result. Businesses may be certain that their efforts are bringing them closer to their targeted goals by keeping track of test data throughout the whole project life cycle.

The Test Metrics Life Cycle consists of several stages:

Test Metrics Life Cycle

1. Analysis

  • Identification of the best metrics for testing purposes.
  • The establishment of quality assurance criteria must be followed.

2. Communication

  • Training the software testing team on the data points that will be collected for metric processing.
  • Providing project requirements to the testing team and stakeholders.

3. Evaluation

  • Obtaining and confirming relevant data.
  • Using the acquired data to assess the metric's worth.

4. Reporting

  • Making a thorough and convincing report summarizing the findings.
  • Soliciting valuable feedback from stakeholders and representatives based on the information supplied.
  • Reports are being distributed to the appropriate representatives and stakeholders.

Example of Software Test Metrics Calculation

Let's consider an example that demonstrates how to calculate various test metrics.

In this scenario, we have gathered specific data during the development of test cases. We will refer to each data point with a unique identifier:

Requirement Count20
Average Test Cases per Requirement10
Total Test Cases Written200
Total Test Cases Executed180
Passed Test Cases100
Failed Test Cases80
Blocked Test Cases0
Unexecuted Test Cases20
Total Defects Identified10
Valid Defects Accepted10
Deferred Defects0
Fixed Defects10

Now, let's calculate some important test metrics based on the provided data:

  • Percentage of Test Cases Executed: To determine this, we divide the number of executed test cases (180) by the total number of test cases written (200) and multiply by 100. The result is 90%.
  • Test Case Effectiveness: This metric is calculated by dividing the number of defects detected (10) by the number of test cases run (180) and multiplying by 100. The value is approximately 5.55%.
  • Percentage of Failed Test Cases: We calculate this by dividing the total number of failed test cases (80) by the total number of executed tests (180) and multiplying by 100. The result is approximately 44.44%.
  • Percentage of Blocked Test Cases: Since no test cases are blocked, the percentage is 0%.
  • Percentage of Fixed Defects: To calculate this, we divide the total number of defects fixed (10) by the number of defects reported (10) and multiply by 100. The result is 100%.
  • Percentage of Accepted Defects: By dividing the number of defects accepted as valid by the development team (10) by the total number of defects reported (10) and multiplying by 100, we get 100%.
  • Percentage of Deferred Defects: As no defects have been deferred for future releases, the percentage is 0%.


In the always changing environment of software development and quality assurance, software testing metrics act as a compass. They offer a numerical framework for assessing the efficiency, efficacy, and general caliber of the testing procedure. Metrics can be used by testing teams to acquire useful information, spot areas for improvement, and make data-driven choices that help produce high-quality software products.

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Frequently asked questions

  • General ...
What are test metrics in software testing?
Software testing uses test metrics, which are quantitative measurements, to assess and track the efficacy, efficiency, and development of the testing process. They provide insightful information and advocate for the use of data-driven decisions to improve software testing outcomes.
What are productivity metrics in software testing?
Software testing productivity measures gauge the effectiveness and efficiency of the testing process. They include information on how resources are used, how long tests take to run, how often defects are fixed, and other elements that help increase productivity and enhance testing efficiency.
What are the QA metrics?
In order to monitor and guarantee the quality of software throughout its development lifecycle, QA metrics are quantitative measurements. These metrics can be used in the requirements collecting, design, coding, testing, and deployment phases of the software development process. They offer perceptions into the efficacy and efficiency of QA actions, assisting teams in making data-driven choices to raise the caliber of software.
Why are metrics used in testing?
Metrics are used in testing to define testing objectives precisely and to determine whether they were met or not. They make it possible for teams to keep tabs on testing activities, progress, productivity, and testing quality. Metrics enable efficient decision-making to produce desired results and help ensure that testing efforts are in line with aims.

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