Software bugs that surface during production can incur substantial losses. Identifying them early is critical—but quality assurance comes with its own financial burden, often consuming up to 25% of a project’s budget. Because of this, ensuring that evaluation methods are both efficient and results-driven becomes a necessity.
Predefined measurement indicators can guide teams in evaluating the effectiveness of their quality assurance strategy. This article outlines nine key indicators and how they’re calculated to support QA professionals in making informed decisions.
Why is testing measurement important?
Testing measurement is a continuous cycle, and it is important because it allows:
- Real-time monitoring
- Monitor productivity
- Identifying areas that require attention
- Measuring changes
- Measuring software quality changes
Testing measurements should be done for both manual and automated tests. In particular, automation brings scalability, precision, and speed. For more information on automated testing, refer to software testing best practices.
Absolute numbers
Absolute numbers are one-dimensional metrics. They have interpretation limitations due to their absoluteness. 2 of the notable ones in this category are:
1- Total Test Duration
This metric measures the time it takes to conduct tests. Ideally, you want the test duration to be low, as it will mean that you can run more tests.
This is important because in an agile development system, many iterations are made, and if testing takes a long time, it will decrease the development speed and can cause developers to test less, which can lead to higher bugs and problems down the road. The downside of this metric is that it does not show the quality of the test.
2- Total number of defects
Tracking the total discovered flaws across cycles allows comparison across releases. Still, quantity alone doesn’t account for severity or root causes.
All of the following metrics are derived metrics.
Tracking & efficiency
The following measures help QA teams to understand the efficiency of their testing and track their progress against their goals and milestones. However, these ratios do not provide information about the quality and coverage of a test.
3- Test passed percentage
It is an overview of the testing progress. It can be powerful when used in combination with defect KPIs. For example, if the defect escape ratio is high and the test passed percentage is high it can mean that tests are not covering important areas. (see Figure 2)
Figure 2
4- Fixed defects percentage
It indicates how many of the defects found were fixed. ( see Figure 3)
Figure 3
Test effectiveness
Test effectiveness ratios are quality tests. They measure the effectiveness of the tests in finding bugs and defects.
4-Total defect containment efficiency:
This ratio indicates how effective your testing process was in finding defects. A high ratio is desired, and a low ratio means that the testing process needs improvement. Defects in the production stage are costly, so this is a last-resort ratio. ( see Figure 4)
Figure 4
Test coverage
Test coverage metrics are designed to understand what portion of the total project/application has been tested. Coverage ratios do not provide information about the quality of the tests. They can be used to understand if the team is behind, on, or ahead of the testing schedule.
5- Automation test coverage
It tracks what portion of the total tests are automated. It can be used by executives to measure their automation plans and progress. ( see Figure 5)
Figure 5.
6- Test execution coverage:
How much of the required tests have been covered so far. ( see Figure 6)
Figure 6.
7- Requirement coverage
This ratio shows how much of the requirements have been covered. ( see Figure 7)
Figure 7.
Defect metrics
Defect metrics deal with defects in different ways as they are categorizable in different ways, such as severity, type, root cause, etc.
8- Defect density
This measure tracks the total number of defects over the size of the release. This ratio indicates whether the build is ready for release or requires further testing. A good defect density ratio is considered 1 defect per 1000 lines of code. ( see Figure 8)
- Consider the complexity of the code.
- Developer’s level of skills. A highly skilled developer team might have optimized the code, so there are fewer lines of code; in this case, the defect density ratio might be high. A lower-skilled developer team might have written many lines of redundant codes; in this case, defect density can stay constant or go down.
Figure 8.
9- Defect severity index
It measures product quality. The QA team can use this metric to understand the effect of negative impacts on software quality and performance. ( see Figure 9)
Figure 9
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