9 QA Metrics That Matter the Most in Software Testing (With Examples)

Software testing is the backbone of software development, ensuring every line of code works as intended and delivering a product that’s reliable, efficient, and user-friendly.

For years, organizations have allocated over 20% to 30% of their annual IT budget toward quality assurance and software testing. And for good reason - QA testing is the foundation of your software and essential for stability. Software testing acts as a safety net, catching bugs and errors before they reach the user, and saving developers from costly fixes down the road. Beyond functionality, it also ensures the software meets quality standards and performs well under different conditions.

Since the success of a software product’s release hinges on QA, you need to implement metrics that gauge progress and achievements. But with countless metrics available, how do you determine which ones matter and which are just noise? 

In this article, we explore the basics of QA metrics, why they matter, as well as how to choose metrics tailored to your QA strategy needs. 

What are QA metrics and why do they matter?

QA metrics assess software effectiveness, quality, and reliability during testing. They provide insights for improvement and help track and monitor processes. 

As the saying goes, “If you can’t measure it, you can’t improve it.” 

While metrics will hardly be the most exciting aspect of your job, they’re essential for: 

• Improving product quality. Identify areas where software might be failing. 
• Ensuring efficiency. Uncover bottlenecks in the testing process.
• Quantifying progress. Set objectives to stay on track. 
• Minimizing risks. Reduce the risk of critical production failures
• Informing decision-making. Adjust based on data, not assumptions.

QA metrics are crucial in software quality assurance, as they turn the reams of complex data into insights. They help you deliver a reliable, high-quality product—much like a compass helps you find your destination. 

9 important QA metrics to track today

Understanding what QA metrics are, and knowing some isn’t the end. Now comes the crucial part—focusing on the metrics that matter most for your team and organization. You can track many things, but they won’t all provide strategy-shaping insight. Here are 9 QA metrics that will enhance your testing strategy: 

1. Escaped bugs

If there’s one thing you can judge your entire QA process on, it’s the number of bugs your customer reports. 

Ideally, users wouldn’t encounter any issues post-release, but in reality, some bugs are sneaky and escape testing. Often, they can slip past numerous quality checks.  

Yet, if too many issues reach users, it signals that your testing suite needs improvement. Smaller issues that don’t plague the user may be tolerable. However, if users report high numbers of critical bugs, it will damage their trust in you and will incur costly fixes.   

2. Test coverage 

Test coverage is the percentage of the application verified by tests, a key metric for assessing QA quality. By measuring test coverage, you confirm that you’ve examined as many parts of the software as possible and reduced the chances of hidden bugs. 

Your test coverage should include all critical features, but it should also cover some smaller aspects. Higher test coverage lowers the risk of defects in untested areas and, thus, the overall product. 

You can measure coverage by calculating the percentage of code tested with this simple formula: 

So, for a program with 1500 lines of code and tests that cover 900 of those lines, the coverage would be 60%. This means that 60% of the code has been tested and 40% hasn’t, suggesting a substantial portion that could be housing bugs. 

3. Test automation coverage

Test automation coverage measures the extent of automated testing versus manual. The more automated a process is, the faster you can run tests, especially for repetitive tasks nobody wants to do. While there will always be a need for manual checks, increasing automation reduces reliance on time-consuming manual tests and allows your QA team to focus on complex areas. 

High automation coverage is particularly valuable for regression testing. You can calculate test automation coverage with this simple formula: 

So, if you have 600 total tests and 390 of them are automated, your coverage would be 65%, i.e. that much of your testing workload is automated. 

You may be interested in: 7 QA Best Practices to Improve Software Testing for 2024.

4. Defect density

Defect density shows the number of bugs per set amount of code, often using the KLOC metric, or thousand of lines of code. In short, it’s a direct measure of the quality of the code. It’s a tester’s way of counting the cracks in a sidewalk. More cracks suggest a bigger problem with the development or testing processes that need addressing. 

Conversely, low defect density signals cleaner code and effective testing. Although it doesn’t mean bug-free code, it reflects fewer critical issues. Here’s a formula to calculate your defect density: 

So, if your software has 50 defects and consists of 8000 lines of code, you’ll first have to convert the lines to KLOC: 

Then, plug those numbers into the formula: 

This defect density would be considered relatively high, so there may be quality issues in the code you should address. 

5. Test cost 

To demonstrate QA’s value to shareholders who don’t understand the nitty-gritty of the job, you need to talk about money. Statista revealed that costs are the biggest challenge for companies wishing to deploy a test environment. Return on investment (ROI) is paramount, as executives need to know the financial implications of your efforts. If the cost of fixing bugs skyrockets after release compared to addressing them during development, you need to highlight this. 

For example, if you spend $5000 and identify 50 bugs, the cost per bug is $100. However, if a user discovers a bug post-release, it costs $1000. This illustrates that testing needs upfront investment and emphasizes that QA isn’t just an operational expense. Instead, it’s a strategic one that impacts the bottom line. 

You may be interested in: How to Choose the Right Quality Assurance Partner?

6. Defect detection rate 

The defect detection rate (DDR) is the percentage of uncovered defects during the testing process from the total number of defects in the software. It measures the effectiveness of the software testing process, i.e. how good your team is at catching bugs before release.  

A high DDR means your efforts are paying off and you’re catching most of your bugs before they reach the user. On the other hand, a low rate suggests that issues have slipped through the cracks and will need fixing after launch. 

The formula for calculating DDR is: 

Let’s say your team executed 200 test cases and found 40 defects during the testing phase. Post-release, your users reported more defects, with a total number of 50. Your calculations would then show an 80% DDR, which is a strong rate. 

7. Test reliability

Test reliability measures the consistency of a test to yield the same results over time under the same conditions. For QA, it means that if a test is reliable, it produces similar outcomes when repeated. Reliable tests indicate a product's stability and the QA team’s effectiveness in doing their jobs.  

High reliability means the team can trust the accuracy of the test and make confident decisions about quality. Low reliability means you’d need to retest and make time for additional checks. As you can imagine, the latter hampers your processes and increases risks all around.

8. Bugs found vs. fixed 

This metric measures the efficacy of your QA process by tracking the number of bugs identified during testing compared to the number resolved. 

These numbers tell you whether the testers detect the bugs on time and help the team identify patterns. For example, the bug fix percentage can reveal that most of your bugs are solved in a particular stage of the process, which could offer significant insights into processes. 

There are a few ways to measure this metric, including manual counting and calculating the percentage, which you can do with this formula: 

And here’s what that would look like if a team tracks bugs over four weeks: 

By the end of this cycle, this team found 80 bugs and resolved 54, leading to a bug fix rate of almost 70%. Over time, you can compare this percentage to determine whether the testers are improving. 

You may be interested in: Best Practices for Effective Bug Reporting in Software Testing.

9. Schedule variance

Lastly, you should measure how close your project is to its planned timeline to ensure you’re on track. You do this by measuring the schedule variance (SV), which tells you whether you’re ahead of schedule, on time, or behind. 

You get this insight by comparing completed work with planned milestones. This higher-level metric tracks progress, facilitates resource planning and mitigates risks. Given testing schedules are often strict, falling behind can disrupt the entire release cycle. Schedule variance also helps you: 

• Plan releases better and align better with overall timelines. 
• Prioritize QA activities and focus on high-priority test cases.
• Improve process efficiency and identify bottlenecks.
• Detect issues quickly to avoid critical bugs.

Final thoughts

Tracking the relevant QA metrics should be non-negotiable, as they showcase that your testing efforts are thorough and consistent. While there are a myriad of other metrics you can track, it’s crucial to pick those that can get you closer to your goal. If you use them strategically, these metrics will empower your team to improve, adapt, and work with an overarching goal in mind: delivering software that meets and exceeds expectations. 

Ready to take your QA process to the next level? Start tracking the metrics that matter. Contact us to learn more about our software quality assurance services and how they can benefit your project.