Quality Assurance Metrics That Every Team Should Track

Poor testing practices lead to quality problems on more than 80 percent of software projects. This is what makes Quality Assurance Metrics vital in any team. These metrics enable you to gauge performance, identify areas for improvement, and enhance the quality of your products. Measuring the appropriate QA metrics results in a decrease in bugs, quicker releases, and happier customers.

For example, apps that have uptime above 99.9% are 26% more likely to retain users. This demonstrates that improved QA value leads to enhanced performance, increased retention, and accelerated growth.

How to Choose the Right Quality Assurance Metrics

The following are a few guidelines to choose the right quality assurance metrics:

SMART Criteria for QA KPIs

Proper QA metrics should adhere to the SMART rule:

• Specific - The metric is to be straightforward (e.g., defect leakage rate).
• Measurable - It has to be quantifiable by numbers.
• Attainable - The goal must not be unrealistic.
• Relevant - It has to be associated with software quality, rather than arbitrary activities.
• Time-bound - It should be measured in a set time, such as per sprint or per release.

In that manner, teams do not become confused and quantify only what is really important.

Mapping Metrics to Maturity Levels (CMMI vs. TMMi)

Models that demonstrate the level of advancement of the processes of a particular company are CMMI ( Capability Maturity Model Integration) and TMMi (Test Maturity Model Integration).

• CMMI examines the general level of maturity of the organization.
• TMMi lays emphasis on testing and QA maturity only.

At the low level, a team can monitor easy indicators such as the number of successfully passed tests. On a higher level, they have an opportunity to measure such advanced measures as defect leakage or mean time to repair. In this manner, metrics increase as the team continues maturing.

Avoid Vanity Metrics

Certain measures appear fine, but do not reflect actual quality. These are referred to as vanity metrics. For example, when one writes 1,000 test cases, it does not necessarily mean that the software is free of bugs.

It is more important to know whether or not those test cases find actual bugs. Therefore, teams are advised to monitor useful metrics such as customer-reported defects or uptime rather than huge numbers that are merely impressive.

10 Must-Track Quality-Assurance Metrics

The following are the 10 must-track quality assurance metrics:

1. Test Coverage

Test coverage reflects the extent to which the code or features are covered. It is determined by dividing the number of items tested by the total items and multiplying the result by 100. The typical target is 80-90% on vital code and at least 60% on other code.

It is measured by using tools like JaCoCo, Istanbul, Coverage.py, SonarQube, and Cobertura. Inclusion of a coverage badge in the README file assists developers in maintaining high coverage.

2. Defect Density (defects/KLOC)

Defect density refers to the number of bugs in every 1,000 lines of code. It is obtained by dividing the number of defects by the size of the code in thousands of lines. Strong level is less than 0.5 in case of stable projects, and less than 1.0 in case of new projects.

To track it, Jira, Azure boards, SonarQube, and Bugzilla are commonly used. When defect density is plotted on a heat map, it makes it clearer what parts of the code require additional examination.

3. Defect Removal Efficiency (DRE)

Defect removal efficiency indicates the number of bugs fixed before release relative to after release. It is determined by the number of defects that occur before the release divided by the number of defects that occur after the release, multiplied by 100. The target is at least 95 percent in SaaS and at least 98 percent in medical or financial software.

This can be measured using defect trackers with release tags or using Excel in small teams. The fact that post-release defects are counted after 30 days is to prevent confusion with new feature requests.

4. Mean Time to Detect (MTTD)

MTTD is the mean time it takes to detect a bug once it is in the system. We find it by summing the difference between the time each bug was created and the time when it was found, and dividing it by the total bugs.

​This should not exceed 4 hours in strong pipelines and under 24 hours in normal environments. Teams monitor it using Jira dates, Git commit logs, or alerting tools such as Datadog.

5. Mean Time to Repair (MTTR)

MTTR provides the mean time to repair a bug after it has been discovered. It is determined by summing together the time between detection and closure of all bugs and dividing by the total number of bugs.

It is a good practice to fix critical bugs within less than 8 hours and medium ones within 48 hours. This is typically tracked by Jira workflows, GitLab analytics, or such tools as PagerDuty.

6. Escaped Defects (Post-release)

Escaped bugs are bugs that are reported by users after a release. We compute this by the number of bugs labeled as being escaped that are created after the release date.

In business software, strong teams prefer less than 0.5 percent of users affected or fewer than three defects per release. Zendesk, Intercom, Jira, and log tools are used to monitor these cases.

7. Automation Coverage

Automation coverage is a measure of the portion of the total testing that is performed automatically. It is determined by dividing the number of automated test cases by the overall number of test cases and then multiplying it by 100.

Firms desire 100% smoke tests and 70-80% regression tests but they leave some exploratory tests manual. It uses tools like Selenium, Cypress, Playwright and Jest.

8. CI Build Success Rate

This measure represents the number of times software builds are done without failure during all stages, including compilation, unit tests, security tests, and packaging. It is calculated by dividing successful builds by the total builds and multiplying by 100. Indicatively, 95 successful builds out of 100 would mean a success rate of 95.

The benchmark should be over 95 percent in the case of the main branch and over 90 percent in the case of feature branches. GitHub Actions, GitLab CI, Jenkins, or Azure DevOps are used by teams to measure this rate. A high success rate indicates that your pipeline is stable and developers are able to release code at a faster pace.

9. Requirement Volatility

Requirement volatility demonstrates the extent to which the planned work is altered within a sprint or release. It is a count of the number of user stories whose scope was altered when the work was already initiated. This is calculated by taking the number of stories changed divided by the number of stories committed, then multiplying by 100. For example, when 2 stories out of 20 are altered, the volatility is 10.

In the case of stable products, the benchmark is below 10%. In the case of start-ups or research ventures, it may reach 20%. This can be quantified using Jira history logs, Azure Boards, or Trello card actions. High volatility usually implies poorly planned or shifting priorities and which may lead to delays in sprints.

10. Customer-Reported Issues (30-day)

It is a measure of the number of bugs or problems that are reported by customers directly after a release. It targets the initial 30 days after launch since this is the period when issues are most apparent. It is calculated by summing all support tickets with defects reported within 30 days of release.

Powerful teams want to make this figure less than 0.3% of monthly active users. That is, a very small portion of the users should experience problems.

How to Put Together Metrics in Quality Assurance

Following is a detailed step-by-step approach to put together metrics in quality assurance:

1. Start with your goals. Choose what is the most important to your business, which can be speed, cost, customer satisfaction, or stability. This provides guidance to your QA metrics.
2. Pick the right mix. Select a balance of process measures (such as build success rate), product measures (such as defect density), and customer measures (such as reported issues). This provides a complete picture rather than one-sided information.
3. Keep it simple. Do not overcrowd your team with numbers. Monitor a few important indicators that demonstrate improvement.
4. Add context. Always describe the meaning of the metric. It is not a number that matters, but the team has to know why it matters.
5. Make it visible. Provide dashboards, reports or at least basic charts to make the metrics visible in real time. Ownership is created through transparency.
6. Review together. Metrics are not to be merely a tool. Talk about them during team meetings, sprint reviews, or retrospectives to transform numbers into action.
7. Adjust over time. Your QA objectives evolve with your product. Adjust the metrics to fit your current needs.

Common Pitfalls & How to Avoid Them

Excessive monitoring causes metric fatigue. Keeping too many numbers may puzzle teams. Pay attention to the most beneficial QA metrics.

False positives are caused by ignoring context. The number of defects might seem large; however, it might be normal when the features are growing rapidly. Always interpret metrics in context.

Using tools alone is not enough to capture the people's side. Good QA does not only concern dashboards. Discussions, audits, and collaborations are as important as statistics.

“A metric without a conversation is just a number.” – Red Star QA Lead.

Why Red Star Technologies is the Best Choice for QA

At Red Star Technologies, your success is important to us. We have just one thing in mind, and that is to provide you with software that is fast, reliable, and leaves your users happy. We undertake every project in the same manner and with the same attention we would have given to ourselves.

You can rely on us as we offer:

• International-level quality checks.
• More efficient tools that will save you time and minimize errors.
• Testing that relates directly to your business objectives.
• A balance of speed and stability with each release.
• Software that you trust to attract customer loyalty.

Final Thought

Quality assurance measures are not numbers alone but the roadmap that keeps your team on course, efficient and customer oriented. Careful selection of metrics is not just a way to minimize bugs, but it is also a way to gain trust and long-term user satisfaction. The point is to quantify what counts, talk about it in your team and improve as your product develops.

Want to advance your QA game? Red Star Technologies will make it easy to establish smarter, people-focused QA metrics that actually make a difference.