What are Software Testing Metrics and Why Should You Care about Them?

Doctors measure our body temperature and blood pressure over time. Businesses measure readership and user behavior on their websites or apps to pull trends and reports. We measure and define success in our personal and professional lives by comparing past results, and the same goes for the software development process. So why would metrics in software testing be any different? 

Understanding Software Testing Metrics

Software testing metrics encompass a wide array of quantitative measures that evaluate different aspects of the testing process. The goal of software test metrics and measurement is to improve the efficiency and effectiveness of the testing process and make better decisions for further testing. 

Although metrics sound like a nightmare to some testers and are associated with extra paperwork (or just seen as a waste of time), having dynamic indicators will provide you with reliable data about the health of your product just as blood pressure testing does for your physical health.

Why Are Software Testing Metrics So Important?

Based on our extensive experience in QA Services, we identified six reasons why you should start using quality management metrics in software testing right now:

Software testing metrics provide our teams and stakeholders with a clear understanding of the state of each product and process. Still, even though metrics are handy, using more software test metrics and measurements doesn’t inherently lead to improving your product’s actual quality. Your goal should always be to have a well-rounded picture of your product at each stage of the software development cycle. That doesn’t mean just gathering every metric you can think of; it means choosing the right ones and measuring them accurately and consistently.

How to Select the Right Software Testing Metrics — and Measure Them the Right Way

To help your QA team select relevant metrics and eliminate unnecessary work, we’ve prepared this handy checklist:

1. Ask yourself the three most important questions:

-What are we measuring?

-How are we measuring it?

-Why are we measuring these metrics?

Don’t use quality assurance metrics that don’t add any value, and make sure you’re scrupulous in the metrics definition process.

Software Quality Assurance Metrics Example 1

We often see teams that use metrics like the number of bugs in the backlog on long-term projects. They say things like, “See how few bugs we have!” In reality, these metrics only show us that we have a QA team that does their job — and this isn’t likely to be a groundbreaking revelation.

On the other hand, tracking the percentage of high/medium/low priority bugs would help to indicate the overall quality of the software that we’re building, which can help us make adjustments down the road.

2. Automate the process of calculating QA metrics as much as possible

By doing this, you will spend less time manually gathering QA metrics, and your metrics will always be up to date without any active effort on your part. For example, use jql if you have Jira, or other tools, depending on what test management/task tracking system you use.

Software Quality Assurance Metrics Example 2

If you need data on critical bugs each sprint, set up a jql request on your Confluence page, and it will be updated regularly.

3. Gather feedback and improve metrics over time

Don’t stop once you’ve gathered and set up all the software testing metrics. Listen to feedback and make your metrics and reports more efficient and informative. Show the QA metrics that you gather to your management and team, establish regular metrics brainstorming sessions, and evaluate the value of the metrics you use on a monthly/bi-weekly/weekly basis.

Types of Test Metrics in Software Testing

Let’s move on to the practical considerations. What can you measure in your project? The immediate answer is everything, but let’s look more closely at the details. These metrics can be categorized into various types, including:

• Product quality metrics: defect density, escaped bugs, enhancements tracking, bugs found per component, etc.
• Defect metrics: These metrics focus on quantifying the number and severity of defects identified during testing. They include reported/fixed defects, time between failure, defects by root cause, source of defect, test release, priority/severity, rejected, duplicated, phase introduced and detected, trends from defect reporting to resolution, number of defect fixes that introduced new defects, etc. This helps gauge the quality of the software under test and track the progress of defect resolution efforts. 
• Test metrics/Execution metrics: These measure various aspects of test execution, such as total tests planned, specified (implemented), run, passed, failed, blocked, and skipped; smoke, regression, and confirmation test status, including trends and totals for regression test and confirmation test failures; hours of testing planned per day versus actual hours achieved; availability of the test environment, etc. These metrics offer valuable insights into the efficiency and effectiveness of the testing process, highlighting areas that may require improvement or optimization.
• Coverage metrics: Test coverage metrics assess the comprehensiveness of testing by measuring the extent to which the code has been exercised by tests. This includes metrics such as statement coverage, branch coverage, risk coverage, code coverage, environment/configuration coverage, requirements and design elements coverage, and path coverage, which provide insights into the effectiveness of the test suite in identifying potential defects. 
• Risk-related metrics: % of risks completely covered by passing tests, % of risks for which some or all tests fail, % of risks covered, sorted by risk category, % of risks identified after the initial quality risk analysis, etc.
• Code Quality Metrics: Code quality metrics evaluate the quality of the codebase from a testing perspective. This includes metrics such as cyclomatic complexity, code duplication, and code churn, which help assess the maintainability, readability, and overall quality of the code being tested.
• Test Automation Metrics: For projects utilizing test automation, metrics related to automation play a crucial role in evaluating the efficiency and effectiveness of automated testing efforts. These metrics include automation coverage, automation execution time, and automation return on investment (ROI), which help measure the ROI of automation initiatives and identify opportunities for optimization.
• Other QA metrics: number of changes required, accepted, built, and tested, planned/actual cost, planned/actual duration, planned/actual dates for testing milestones, planned/actual dates for test-related project milestones, etc.

How Can We Measure Software Testing Metrics?

We understand that the process of measuring metrics in software testing and estimating their effectiveness may seem a bit scary and vague. That’s why it’s best to follow clear instructions and recommendations. Here are a few steps to complete:

• Define your software testing metrics based on specific objectives for the project, process, and product. Keep balance in mind — a single metric won’t give you the full picture of your software quality.
• Track test metrics over time. Automate the collection process, keep data in a shared space such as wiki/Confluence, and review results at regular intervals.
• Report software testing metrics. Report to the customer and the team to show your progress. Improve your reports to make them easy to understand, more useful, and user-friendly.
• Check regularly for QA metric validity. Don’t track metrics that aren’t useful or that show incorrect data.

Real-World Examples of Metrics in Action

To illustrate the practical significance of metrics in software testing, let’s consider a few real-world examples:

• Defect Density: A software development team notices an increase in defect density during a particular phase of testing. By analyzing defect density metrics over time, the team identifies a correlation between the increase in defects and a recent code refactor. Armed with this insight, the team decides to conduct additional testing and code reviews to address potential quality issues introduced during the refactor, ultimately reducing defect density and improving overall software quality.
• Test Coverage: A project manager wants to assess the effectiveness of the test suite in covering critical areas of the codebase. By analyzing test coverage metrics, the manager identifies several code paths that are not adequately covered by existing tests. In response, the team develops additional test cases to improve coverage in these areas, ensuring that potential defects are identified and addressed before they impact end users.
• Automation ROI: A software development organization invests in test automation to accelerate testing efforts and improve overall efficiency. To evaluate the ROI of the automation initiative, the organization tracks automation metrics such as automation coverage, execution time, and defect detection rate. By comparing these metrics to manual testing efforts, the organization determines that test automation has significantly reduced testing time and increased defect detection rates, resulting in a positive ROI for the automation initiative.

The Importance of Metrics Will Only Continue to Grow

In software testing, metrics provide a way to assess the efficiency of a testing effort. Without tracking metrics and measurements in software testing, your team can’t get any feedback or insights. If metrics aren’t serving as your early warning system for issues, you’ll find problems later when fixing them requires more time and effort.  Whether it’s assessing test coverage, tracking defect trends, or evaluating automation ROI, metrics provide the data-driven foundation needed to ensure the delivery of high-quality software products. 

As software development continues to evolve, the importance of metrics in testing will only grow, making it essential for development teams to prioritize metrics and leverage them effectively to achieve their quality assurance goals. That’s why it’s crucial to choose an effective testing strategy that will show gaps and provide you with a clear vision of what QA tools or solutions can be applied to remedy the situation.

Author: Karalina Panada