10 Common Quality Assurance Metrics

The quality of a good or service is very important in the software development process in the current competitive market. Companies can avoid product problems by using the standards and practices set out by quality assurance. This blog post will explain what quality assurance is, why it’s more crucial, testing procedures for assuring software quality, and the advantages quality assurance can provide for a company.

Planning, carrying out, and monitoring tasks are required to manage the quality of a good or service. The monitoring component of that profession includes quality assurance. It is the procedure or action of determining whether or not the company’s quality requirements were satisfied. The manufacturing industry was where the idea of quality assurance initially emerged, and it eventually spread to most of the industries, including software development.

1. Defect Density

Let’s examine what density means in its purest form. It is “the level of a substance’s compactness” (Google). Thus, the compactness of faults in the application is measured by defect density. (All right, so it’s just a more advanced kind of defect distribution.)

Functional areas, or more precisely KLOC, are used to categorize applications. Therefore, bug density is the average number of flaws in a section or per KLOC in a software application.

2. Test Execution and Bug Fixing

The total executions organized as passed, failed, blocked, incomplete, and unexecuted should be displayed in a chart for this measure.

Key Measures:

● Status of test completion.
● Status of test execution at completion.
● Execution productivity for test cases.
● Defect volume.
● Flaw severity and priority.
● Accepting or rejecting flaws.

Why it matters: At this point, comparing the proportion of tasks that have been finished and executed to the total number of cases is useful. The entire team can understand which issues in which modules may have a serious impact on the final product and should be resolved first thanks to defect-related metrics.

3. Test Design

This quantifies the number of test cases produced per day while test design coverage gauges the proportion of test cases covered in comparison to the total number of requirements.

Here, product functionality is examined to find any end-user-side problems. Do any functional voids exist? Exist any obstacles that could prevent users from downloading the app?

These potential issues are located here, and the QA team makes sure that all necessary requirements are fulfilled by test cases. It is carried out to identify functional deficiencies on the end-user side:

● Coverage of test design
● Test design effectiveness

4. Test Coverage

Metrics for test coverage are used to quantify and track your testing activity. Test coverage metrics also assist you in enhancing the testing procedure and maximizing performance by assisting your team in providing answers to crucial issues like the quantity of bugs discovered and the time spent testing. This assesses test effort and provides a general estimate of the application’s test coverage rate.

5. Lead Time

Lead time can provide important information about how effectively the development process operates. Lead time measurements within a team can gauge a team member’s competence level and frequency of deployment. Manufacturing is where lead time metrics first appeared many years ago. For use in agile software development, specifically inside the framework, the definitions have been modified.

Lead time was divided into:

● Fewer than a day (Elite)
● Ranging from one day to one week (High)
● Ranging from one week to one month (Medium)
● Spanning the size months and one month (Low)

6. Product Exploitation and Support

This metric measures testing efficiency and illustrates the number of flaws that were not discovered and corrected prior to production deployment. It aids in determining what product strategy adjustments are required to boost testing efficiency for upcoming releases.

● Effectiveness of defect elimination

7. Test Economics Metrics

Testing expenses include labor, infrastructure, and equipment. A testing team must carefully estimate how much to spend and track how much they really spend, unless they have unlimited resources. This can be aided by some of the QA performance measures listed below:

● Total Allocated Cost: The sum authorized by QA Directors for testing activities and resources for a specific project or time period.
● Actual Cost: The sum actually spent on the exam. Determine this by dividing the total cost by the number of requirements, test cases, or testing hours.
● Budget variance: The difference between the allocated cost and the actual cost.
● Time Variance: The discrepancy between the actual and anticipated times needed to complete testing.
● Cost Per Bug Fix: The amount each developer spends to fix a bug.
● Cost of Not Testing: Let’s say that after going into production, a set of new features needs to be reworked. In this case, the cost of the reworking operations essentially amounts to the cost of not testing.

8. Test Execution Status

For simple comprehension of the test run status, Test Execution displays the total number of executions grouped as passed, failed, blocked, incomplete, and unexecuted. Since individuals are more likely to forget raw figures, these are excellent visual aids for the daily status meeting. The expanding and contracting bars attract attention and effectively convey progress and pace.

9. Defect Severity

This statistic is intended to manage the processes for removing defects and comprehend testing efficiency data. This is more of a category of metrics than it is a specific one. The measurement of the number of defects or bugs based on various parameters is known as defect distribution. These criteria cover things like severity, application area, and even testing style.

10. Production Defect Rate

This measure shows the proportion of failed tests in relation to all the tests that were run. Metrics for software quality assurance must be used to track flaws and organize the process of fixing them. Since it is typically impossible to fix every flaw in a single sprint, problems must be prioritized according to their severity, availability of testers, and a variety of other factors.

Here are some helpful measures for defect distribution:

● Distribution of defects by cause.
● Defect distribution by functional or feature area.
● Distribution of Defects by Severity.
● Distribution of errors by Priority.
● Distribution of defects by type.
● Distribution of defects by type of tester: Dev, QA, UAT, or End-user.

Conclusion

It is crucial to keep an eye on both leading and lagging indicators. Leading indicators allow you to intervene early and make adjustments before things get out of hand, whereas lagging indicators show you the outcomes you’re getting.

We can’t ignore the significance of Quality Assurance (QA) measurements because we have already emphasized their enormous usefulness. The goal of QA metrics is to greatly improve the software testing process. Since they aid in ensuring the precision of the multiple tests carried out by your team, these metrics are crucial in the software development and testing life cycles. When aiming for sustainability, the organization also receives a guarantee of the product’s quality this manner.