Essential Metrics for the QA Process

Quality Assurance (QA) plays a critical role in the software development lifecycle, ensuring that websites and apps meet high standards of functionality and reliability. As digital products have grown more complex, the QA process has evolved into a rigorous, time-intensive activity.

Modern websites and applications require thorough testing to uncover and fix countless bugs, making comprehensive QA essential for delivering quality software.

To ensure the effectiveness of QA, tracking key metrics is vital. These metrics help define success and provide insights into the performance of testing efforts. By establishing measurable goals during the planning phase and closely monitoring these indicators throughout the process, teams can better evaluate the impact and success of their QA activities.

This article will discuss about few essential QA metrics that must be set and observed throughout the process to ascertain its performance.

What are QA Metrics?

QA metrics are measurable indicators used to assess the quality, efficiency, and effectiveness of software development and testing processes.

These metrics help in quantifying various aspects of software quality and can provide valuable insights into the efficiency, reliability, and overall performance of the development and testing efforts.

QA metrics are used to monitor and control the quality of software throughout its development lifecycle. They can be applied to different stages of the software development process, including requirements gathering, design, coding, testing, and deployment.

By tracking these metrics, organizations can identify areas of improvement, make data-driven decisions, and ensure that the software meets the desired quality standards.

What is QA Benchmark?

A QA benchmark refers to a standard or reference point against which the performance or quality of a software development process, product, or testing activity is measured. It involves comparing the metrics and results obtained from the current project or organization with established benchmarks or industry best practices to evaluate performance, identify improvement areas, and set quality assurance goals.

The purpose of using QA benchmarks is to provide a measurable reference point for evaluating and improving software quality. By comparing performance metrics against benchmarks, organizations can:

• Identify gaps and areas for improvement in their current processes or products.
• Set realistic and achievable quality goals based on industry standards or best practices.
• Track progress and measure the effectiveness of quality improvement initiatives.
• Benchmarking can also facilitate benchmarking against competitors, enabling organizations to assess their standing in the market and identify areas where they need to excel or catch up.

The Right Questions to Ask for Determining QA Metrics

Before deciding on what Quality Assurance metrics to use, ask what are the questions these metrics are meant to answer. A few of the questions to ask in this regard would be:

• How long will the test take?
• How much money does the test require?
• What is the level of bug severity?
• How many bugs have been resolved?
• What is the state of each bug – closed, reopened, postponed?
• How much of the software has been tested?
• Can tests be completed within the given timeline?
• Has the test effort been adequate? Could more tests have been executed in the same time frame?

Absolute QA Testing Metrics

The following QA metrics in software testing are absolute values that can be used to infer other derivative metrics:

1. Total number of test cases
2. Number of passed test cases
3. Number of failed test cases
4. Number of blocked test cases
5. Number of identified bugs
6. Number of accepted bugs
7. Number of rejected bugs
8. Number of deferred bugs
9. Number of critical bugs
10. Number of determined test hours
11. Number of actual test hours
12. Number of bugs detected after release

20 Derived QA Testing Metrics

Usually, absolute metrics by themselves are not enough to quantify the success of the QA process. For example, the number of determined test hours and the number of actual test hours does not reveal how much work is being executed each day. This leaves a gap in terms of gauging the daily effort being expended by testers in service of a particular QA goal.

This is where derivative software QA metrics are helpful. They allow QA managers and even the testers themselves to dive deeper into issues that may be hindering the speed and accuracy of the testing pipeline.

Some of these derived QA metrics are:

1. Test Effort

Metrics measuring test effort will answer the following questions: “how many and how long?” with regard to tests. They help to set baselines, which the final test results will be compared to.

Some of these QA metrics examples are:

1. Number of tests in a certain time period = Number of tests run/Total time
2. Test design efficiency = Number of tests designed/Total time
3. Test review efficiency = Number of tests reviewed/Total time
4. Number of bugs per test = Total number of defects/Total number of tests

2. Test Effectiveness

Use this metric to answer the questions – “How successful are the tests?”, “Are testers running high-value test cases?” In other words, it measures the ability of a test case to detect bugs AKA the quality of the test set. This metric is represented as a percentage of the difference between the number of bugs detected by a certain test, and the total number of bugs found for that website or app.

(Bugs detected in 1 test / Total number of bugs found in tests + after release) X 100

The higher the percentage, the better the test effectiveness. Consequently, the lower the test case maintenance effort required in the long-term.

3. Test Coverage

Test Coverage measures how much an application has been put through testing. Some key test coverage metrics are:

1. Test Coverage Percentage = (Number of tests runs/Number of tests to be run) X 100
2. Requirements Coverage = (Number of requirements coverage/Total number of requirements) X 100

4. Test Economy

The cost of testing comprises manpower, infrastructure, and tools. Unless a testing team has infinite resources, they have to meticulously plan how much to spend and track how much they actually spend. Some of the QA performance metrics below can help with this:

1. Total Allocated Cost: The amount approved by QA Directors for testing activities and resources for a certain project or period of time.
2. Actual Cost: The actual amount used for testing. Calculate this on the basis of cost per requirement, per test case or per hour of testing.
3. Budget Variance: The difference between the Allocated Cost and Actual Cost
4. Time Variance: The difference between the actual time taken to finish testing and planned time.
5. Cost Per Bug Fix: The amount spent on a defect per developer.
6. Cost of Not Testing: Say, a set of new features that went into prod need to be reworked, then the cost of the reworking activities is basically, the cost of not testing.

5. Test Team

These metrics denote if work is being allocated uniformly for each team member. They can also cast light on any incidental requirements that individual team members may have.

Important Test Team metrics include:

1. The number of defects returned per team member
2. The number of open bugs to be retested by each team member
3. The number of test cases allocated to each team member
4. The number of test cases executed by each team member

6. Defect Distribution

Software quality assurance metrics must also be used to track defects and structure the process of their resolution. Since it is usually not possible to debug every defect in a single sprint, bugs have to be allocated by priority, severity, testers availability and numerous other parameters.

Some useful defect distribution metrics would be:

1. Defect distribution by cause
2. Defect distribution by feature/functional area
3. Defect distribution by Severity
4. Defect distribution by Priority
5. Defect distribution by type
6. Defect distribution by tester (or tester type) – Dev, QA, UAT or End-user

7. Requirement Defect Density (Defects per Requirement)

This metric measures the number of defects identified for each requirement. It helps gauge how well the requirements were defined and understood by the development and testing teams. A higher density indicates issues in requirement clarity, leading to more defects.

It’s calculated by:

Requirement Defect Density = (Total Number of Defects) / (Number of Requirement)

8. Test Reliability

Test reliability reflects the consistency of test results over multiple test cycles. It ensures that tests produce the same results regardless of when or who executes them, highlighting the stability and robustness of the test cases.

Test reliability is monitored by tracking the number of consistent passes or failures across test executions.

9. Test Cost

Test cost refers to the overall expense incurred in the testing phase of software development. This includes the cost of tools, personnel, and resources involved. Monitoring test costs helps in managing budgets and assessing the financial impact of testing.

It’s calculated by summing up all the costs, including tools, resources, and time spent on testing.

10. Cost Incurred per Bug Fix

This metric shows the average cost required to resolve each defect. A high cost may indicate inefficiencies in the testing process or inadequate test planning.

It is calculated by:

Cost Incurred per Bug Fix = Total Testing Cost / Number of Bugs fixed

11. Time for Testing

Time for testing tracks the total duration of the testing phase, from initiation to completion. It helps in managing project timelines, ensuring that testing activities are completed within the allocated timeframe.

It is simply the time recorded from the start of testing to its completion.

12. Bugs Found vs Bugs Fixed

This metric compares the number of bugs identified during testing to those that have been successfully fixed before the product release. It provides insight into how effectively the development and QA teams are handling defects.

A higher fix rate indicates efficient resolution, whereas a low rate may signal bottlenecks in the bug-fixing process.

It is calculated by comparing the number of defects found to the number of defects resolved.

13. Defect Resolution Percentage

The defect resolution percentage measures the proportion of identified defects that have been fixed. This is a key indicator of the development team’s ability to address and fix issues.

It’s calculated as:

Defect Resolution Percentage = (Defects resolved / Total defects identified) * 100

14. Defect Age

Defect age refers to the amount of time taken to resolve a defect from the moment it is identified. This metric highlights delays in the defect resolution process and can help pinpoint areas where improvements are needed to speed up bug fixing.

It’s calculated by

Defect Age = Difference between when the Defect was logged and when Defect was resolved.

15. Defect Leakage

Defect leakage represents the number of bugs that escape the testing process and are found after the software is released. A high leakage rate indicates weaknesses in the testing process, which could result in poor user experiences post-release.

It’s calculated as:

Defect Leakage = (Post-release defects / Total defects found) * 100

16. Test Case Productivity

This metric measures the efficiency of the testing team by evaluating the number of test cases executed per unit of time. It helps identify bottlenecks and improve the speed of test execution.

It’s calculated by

Test Case Productivity = Dividing the total number of test cases executed by the time taken to execute them.

17. Test Completion Status

Test completion status tracks the progress of testing activities by comparing the number of test cases completed to those planned. It ensures that testing is on schedule and helps identify any delays.

It’s calculated as:

(Test cases completed / Total test cases planned) * 100.

18. Test Review Efficiency

This metric assesses the effectiveness of test reviews in catching errors or gaps before the execution phase. It ensures that the test cases are comprehensive and correctly designed.

Review efficiency is calculated as:

(Errors found during reviews / Total errors) * 100.

19. Test Automation Percentage

Test automation percentage measures the ratio of automated tests to total tests, providing insight into how much of the testing process has been automated. A higher automation percentage can lead to faster execution and greater test coverage.

It’s calculated as:

(Automated test cases / Total test cases) * 100.

20. ROI of Testing

The return on investment (ROI) of testing evaluates the financial value testing adds to the overall software development process. It helps teams assess whether the cost of testing is justified by the benefits it delivers, such as improved quality and reduced defects.

It’s calculated as:

ROI of Testing = (Benefits from testing – Cost of testing) / Cost of testing * 100.

Why use BrowserStack Test Management Tool to improve Automation Test Coverage?

Using the BrowserStack Test Management Tool can enhance automation test coverage through several key features:

• Centralized Test Management: Organizes all test cases in one place for improved oversight.
• AI-Powered Test Case Creation: Uses AI suggestions to optimize and enhance test case quality.
• Integration with Automation Frameworks and CI/CD Tools: Supports over 15 frameworks and integrates with Jenkins, Azure Pipelines, CircleCI, and more.
• Quick Import: Easily imports test cases from platforms like Xray, TestRail, and Zephyr, with custom field mapping options.
• Two-Way Jira Integration: AI-driven binding with Jira for full visibility and better test case management across both platforms.
• Scalable Testing: Facilitates enterprise-scale testing across multiple devices and platforms.
• Security and Compliance: Ensures enterprise-grade data protection with compliance to industry standards.
• Single Sign-On (SSO) Integration: Simplifies user access control with SSO support.
• Real-Time Collaboration: Promotes team collaboration with real-time data sharing and insights.
• Detailed Reporting: Provides comprehensive reports on test coverage and performance metrics.

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Conclusion

Pinning down the right metrics, and using them accurately is the key to planning and executing a QA process yielding the desired results. QA metrics in Agile processes are especially important since managers have to pay close attention to the most minute goals being worked towards and met in each sprint. Polished and specific metrics helps testers stay on track, and know exactly what numbers they have to hit. Failing to meet those numbers means that managers and senior personnel need to reorient the pipeline. This also enables the effective use of time, money, and other resources.

Needless to say, the entire QA process hinges on the use of a real device cloud. Without real device testing, it is not possible to identify every possible bug a user may encounter. Naturally, undetected bugs cannot be tracked, monitor, or resolved. Moreover, without procuring accurate information on bugs, QA metrics cannot be used to set baselines and measure success, This is true for manual testing and automation testing.

Use BrowserStack’s cloud Selenium grid of 3500+ real browsers and devices to run all requisite tests in real user conditions. Manual testing is also easily accomplished on the BrowserStack cloud. Sign Up for free, choose the requisite device-browser combinations, and start testing.

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