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A Detailed Guide on Software Testing Metrics to Gauge Quality Assurance

A Detailed Guide on Software Testing Metrics to Gauge Quality Assurance

March 3, 2023
Sohaib Zaidi

What are Software Testing Metrics?

Software testing metrics are numbers that show how effective and efficient a software testing process is.

Test metrics in software testing help you find answers to some of the your most pertinent testing questions, such as:

  • How long will the overall testing effort take?
  • What is the cost of testing?
  • How bad are the bugs?
  • How many bugs were fixed? How many reopened? How many are closed? How many deferred?
  • How many flaws did the testing team miss?
  • How much time will regression testing take?
  • Can the software updates be delivered on time?
  • Did we test enough? Was there room for more testing in this release?
  • Was coverage adequate with respect to effort?
  • What was the cost of testing?

Why are software testing metrics important to businesses?

There’s a famous saying that you can't improve what you don't measure.

Software testing metrics empower companies to track the progress, quality, and productivity of their testing teams. Further, they can be used to improve the testing process by setting a baseline to track and improve on over time.

With the help of well thought out QA metrics in software testing, organizations can

  • Better determine which testing enhancements and tools are needed to release bug-free software
  • Make better informed decisions regarding cost and time estimates
  • Pinpoint areas of weaknesses in their testing processes to improve upon.

Software Testing Metrics: The Fundamentals

Broadly speaking, QA metrics consist of absolute numbers and derivatives. Both of these metrics can be used to measure testing quality as well as product quality.

Absolute Numbers

Absolute, or whole numbers, provide a general, high-level view of the effectiveness of your testing processes. The data is shown in absolute units, so while it’s good to know, it has some limitations.

Here are some popular absolutely testing metrics

  • Total test duration: Refers to the time it takes to conduct tests. In general, teams should strive to make this number lower, and QA teams should run as many tests as possible during this duration.

  • This measure is important because testing is often the biggest bottleneck to software development. Therefore, the lower this metric, the faster teams can iterate and release software. The downside of this metric is that it does not show the quality of the tests–just the time.

  • Test executions: Using this metric, stakeholders can easily understand how many tests have been run and how many are still pending. As this is a velocity KPI, it allows you to raise a red flag, if the targets aren't reached.

  • Total number of defects found: The number of flaws in each iteration. This makes it possible to compare several versions.

  • Number of planned test hours: This refers to the estimated amount of time required to perform testing activities for a project. It is an important metric as it helps to ensure that testing activities are completed on time and within budget. This number should be compared to actual test hours.

  • Number of actual test hours: The total amount of time spent on testing activities during a project's execution. This includes the time spent on preparing test cases, executing tests, reporting defects, retesting fixes, and other testing-related activities.

  • Number of tests that passed: Refers to the total number of test cases that have successfully executed without encountering any failures or defects. This metric is an important indicator of the quality of the software being tested and provides insight into the effectiveness of the testing process.

  • Number of tests that failed: Refers to the total number of test cases that did not pass or execute successfully during the testing process. When a test case fails, it indicates that the software has a defect or a functionality issue that needs to be addressed.

  • Number of bugs found before release: The total number of defects or issues identified during the testing process before the software is released to end-users. This metric is an essential indicator of the quality of the software and the effectiveness of the testing process.

  • Number of bugs found after release: Refers to the total number of defects or issues identified in the software after it has been released to end-users. A higher number of bugs found after release indicates that testing needs to be improved.

Derivative metrics

Absolute testing metrics simply aren't enough to gauge how well your testing processes are working. For instance, they might tell you how many test cases you run in a day, but they won’t reveal your test coverage. To get a more holistic view of your testing processes, you need to consider derivative metrics.

Derivative metrics involve the use of formulas. By leveraging derivative metrics, you can get a better sense of what’s working, and not working, in your testing pipeline.

  • Test Efficiency is measured by derivative metrics. Here are some key derivative metrics regarding test efficiency:

    • Passed test cases coverage: Refers to the percentage of successful test cases. Past test cases percentage = (Number of passed test / Total number of tests executed) X 100

    • Percentage of fixed defects: This metric is used to identify the percentage of defects that are fixed.

      Percentage of Fixed Defects = (Defects Fixed / Defects Reported) x 100

    • Percentage of total critical defects: This metric shows how many defects are deemed critical, out of all defects. Larger numbers indicate something to monitor.

      Percentage of total critical defects = (Critical defects / Total defects reported) x 100

    • Percentage of total critical defects: This metric shows how many defects are deemed critical, out of all defects. Larger numbers indicate something to monitor.

      Percentage of total critical defects = (Critical defects / Total defects reported) x 100

    • Defect containment efficiency: This metric is used to measure how effective your testing team is at finding bugs in the testing phase.

      (Bugs found in testing phase) / (Total bugs found) X 100

    • Average time taken to rectify defects: Shows how long it takes your team to fix defects.

      Average time = (Total time taken for bug fixes / Number of defects)

  • Test effort: These metrics provide a comparison between the actual effort put out by the testing team versus what was anticipated before the beginning of the testing process. This helps you set baselines with which the final test results will be compared to.

    • Number of test runs per time period = Number of tests run / Total Time

    • Test design efficiency = Number of tests designed / Total time

    • Bug find rate: This metric indicates how many bugs or defects the team discovered,

      per hour Bug find rate = Total number of defects / Total number of test hours

  • Test effectiveness: This metric monitors the bug-finding ability of your team and the quality of your tests. The percentage shows how many of the total bugs found were caught by your testing team in the testing process. It helps answer the question of whether your team was focused on the right areas where bugs are likely to be found.

    • Test effectiveness = (Bugs detected in testing process / Total number of bugs found in testing process + after release) X 100

    A higher test effectiveness number indicates better quality testing, which means you’ll have to spend less effort maintaining tests, in the long run.

  • Test coverage: This metric measures how much an application has been put through testing. Said another way, test coverage helps figure out if our test cases are doing a good job of testing the software code, and how much of the code is being tested when we run those tests.

    Here are some derivative statistics that measure test coverage:

    • Test execution coverage percentage = (Number of tests runs/Number of tests needed to be run) X 100

    • Requirements Coverage = (Number of requirements coverage/Total number of requirements) X 100

    These are normally shown as a percentage value, and indicate how comprehensive your team’s testing was.

  • Test economics metrics: Testing is influenced by several factors including number of people participating, tools, resources, and infrastructure. Test economics metrics helps you understand how much you intend to spend in testing activities and how much you actually end up spending. This can be done by using:

    • Total projected costs of testing

    • Actual costs of testing

    • Variances from the budget estimate

    • Variances from the schedule

    • Average cost of a bug fix

  • Test team matrics: Using these metrics, you can easily determine how much work each member of the team is assigned. These metrics also help you understand if team members require clarifications regarding projects.

    • Number of defects returned per team member

    • Number of open bugs to be retested by each team member

    • Number of test cases allocated to each team member

    • Number of test cases executed by each team member

  • Defect Find Rate Tracking: The percentage of tests that fail compared to the total number of tests run. Comparing the total number of defects and test execution rates will provide an early warning sign that testing processes need to be adjusted to achieve targets.

  • Defect density: Helps QA teams to determine the total number of defects found in a software during a specific period of time of operation or development. The team can determine whether the software is ready for release or whether additional testing is necessary by dividing the results by the size of that particular module.

What are the benefits of leveraging Software Testing Metrics?

Truly understanding software testing metrics and improving on them, helps organizations…

  • Improve overall testing process
  • Measure the overall quality of the test scenarios and cases.
  • Help you measure risk coverage>
  • Help in estimating testing cost
  • Highlight "hotspots" that need improvements
  • Manage the workloads
  • Reduce testing cycles
  • Improve software quality
  • Increase ROI
  • Better forecast when software will be released

Which test metrics improve when organizations switch from manual to automated testing?

Thousands of enterprises are embarking on test automation as it saves time and effort while reducing risk, when compared to manual testing.. To evaluate the effectiveness of automated testing, it's crucial to have baseline metrics (from manual testing) that you can compare to. With the right metrics, you can determine whether your organization is getting an acceptable return on investment from test automation.

Here are some metrics that can quickly help you measure the success of test automation:

  • Total test duration: How long was it taking you to test before automation? How long is it now taking you to test? This is especially important since testing is a common bottleneck in the agile development cycle.

  • Test coverage: Has your test coverage increased since moving to test automation? Many organizations use this statistic to measure how well protected from risk they are.

    Some intelligent automation platforms, like Opkey, utilize test and process mining to help organizations quickly figure out where there are gaps in coverage.

  • Percentage of automated tests: Highlights the percentage of test coverage achieved by automated tests, as compared to manual testing. Helps quantify the progress of test automation initiatives.

  • Defects in production: By analyzing this metric, organizations can understand if the quality of their software has improved with test automation.

  • Overall costs: By comparing the cost of testing before and after automation, organizations can quickly calculate the ROI.

Read more: How to Save Money with Test Automation


To improve the quality of your testing, it’s vital that you understand your testing metrics. 

Opkey helps organizations quickly understand their most important testing metrics–such as their test coverage–and is proven to help improve those metrics. 

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