Today, a software product’s popularity and success are mostly determined by its quality, which has significantly raised the requirement for efficient quality assurance techniques. Software testers are adopting a defined method of monitoring their objectives and effectiveness to accomplish this, which has been made feasible by the usage of numerous software testing metrics.
Metrics are quantitative measurements that offer a thorough knowledge of how well a project or program is performing. It’s critical to evaluate the project’s efficacy, cost, and quality when developing software. Metrics from testing give insight into the general condition and availability of the product, which aids in making choices for the upcoming phase of activities. Additionally, they aid in locating testing process gaps, enhancing testing efficiency.
1. Why monitor testing metrics?
Establishing proper metrics to manage, track, and report testing progress is always advised. Without specified and recorded metrics, the team won’t be able to comprehend the test coverage and quality of the application, rendering it very difficult to make any strategic decisions on subsequent steps like releases, process changes, etc.
2. Major Software Testing Metrics
Software testing metrics, sometimes referred to as software test measurement, demonstrate the breadth, depth, height, and capacity of a software process and attempt to immediately increase its efficacy and efficiency. The most effective way to gauge and keep track of the numerous testing tasks carried out by the team of testers throughout the software testing life cycle is using testing metrics. Additionally, it aids in communicating the outcome of a forecast based on a combination of data. As a result, software engineers across the world employ the following distinct software testing metrics:
2.1 Test Coverage
An essential indicator for determining how well the whole functionality of a software product is covered is test coverage. It denotes the end of testing operations and serves as a standard for testing conclusions. By using the following formula, it may be calculated:
Test Coverage= Number of found defects/Number of expected defects
Another crucial formula included in the calculation of this statistic is: (Requirement Coverage=Number of requirements covered / Total number of requirements) x 100
2.2 Requirement Coverage
To evaluate the degree to which the test cases satisfy the requirements.
This statistic aids in guaranteeing adequate test coverage for the specifications. The formula used for this is:
Number of test cases mapped to requirements / total number of requirements*100
2.3 Test Requirement Coverage
It enables us to estimate both the overall number of test cases completed and the number of test cases still in progress. This statistic, which defines testing coverage, is calculated with the following formula, and measured during test execution: Test Execution Coverage is calculated as follows:
(Total Number of Executed Test Cases or Scripts / Total Number of Executed Test Cases or Scripts) x 100
2.4 Test Percentage of Critical Defects
Calculate the percentage of critical bugs. By counting the number of major bugs, the metric aids in comprehending a product’s quality.
Formula= (Number of critical defects / Number of defects reported overall) * 100
2.5 Derivative Measurements
Derivative metrics enable the team to take effective actions that improve the accuracy of testing by assisting in the identification of the many sections in the software testing process that have problems.
2.6 Defect Density
Defect density, a crucial software testing indicator, aids the team in calculating the total number of flaws discovered in a piece of software during its operation or development. The team can then determine whether the program is ready for release or whether additional testing is necessary by dividing the results by the size of that module. Software defect density is measured in terms of defects per thousand lines of code, or KLOC. The calculation is as follows:
Defect Density = Defect Count/Size of the Release/Module
2.7 Defects Leakage
Defect leakage is a crucial parameter that the testing team must monitor. Before the product is put through user acceptance testing, software testers employ defect leakage to assess the effectiveness of the testing process. Defect or bug leakage occurs when any flaws go unnoticed by the team and are discovered by the user.
Defect Leakage is calculated as:
(Total number of defects discovered in UAT / total number of defects discovered prior to UAT) × 100.
2.8 Defect Removal Efficiency
Defect removal efficiency (DRE) measures how well the development team was able to fix different software bugs before the software was released or put into use. DRE is calculated throughout and between test phases and is measured for each test type. It shows the effectiveness of the various defect removal techniques used by the test team. Additionally, it is an implicit evaluation of the software’s performance and quality.
DRE = Number of defects resolved by the development team/ (Total number of defects at the moment of measurement)
2.9 Environment Downtime
Determine the number of hours that a critical application module, QA environment, or production environment was unavailable. The measure will aid in comprehending the stability of any situation.
The number of hours during which the environment was unavailable is termed as environment downtime.
2.10 Schedule Variance
The difference between anticipated and actual estimates. This indicator shows if the project schedule is on time or behind schedule.
Schedule Variance = Actual value – Estimated value
2.11 Effort Variance
The difference between planned and actual efforts. This measure shows the discrepancy in hours between the estimated and actual efforts.
Effort Variance = Actual effort – Estimated effort
2.12 Test Case Productivity
The quantity of test cases created by the testing team and the amount of time and effort put into the process are measured and calculated using this metric. It serves as a starting point for future measurement and estimate and is used to calculate the productivity of test case design. The following formula is typically used to measure this:
Test Case Productivity = (Number of Test Cases / Efforts Spent for Test Case Preparation).
2.13 Test Automation ROI
To determine the amount of time and effort saved over time on manual testing because of the time and effort put towards automation. The metric assists in comprehending the value addition that test automation brings to the project.
Formula= [Manual execution time * No. of iterations] – [Development efforts + Maintenance efforts]
2.14 Test Automation Stability Rate
To comprehend how stable the test scripts are.
Formula= [No. of false failures / Total no. of test scripts] * 100
2.15 Test Team Metrics
The team defines the metrics for the test team. This statistic is used to determine whether the workload spread among the test team members is equitable and to see whether any team members need further explanations or information about the project or the testing procedure. This statistic is very useful since it encourages team members to share information and gives them the freedom to discuss pertinent project information without pointing fingers or assigning blame. This is accomplished with the help of the following elements, which are shown in the shape of graphs and charts:
- Defects that have been returned are provided to team members together with other crucial information, such as defects that have been reported, accepted, and rejected.
- Each member of the test team is given a set of open defects to retest.
- Each member of the test team was given a test case.
3. How can the metrics be monitored?
Although tracking these data may require some work on the testing team’s part, the benefits far exceed the disadvantages. Project management solutions like Jira, Azure DevOps, and Rally, which offer live dashboards for many of these KPIs, are widely accessible in the market to make the tracking of these metrics simple. There are a few reporting tools like Power BI, Tableau, etc. that can be used in conjunction with this project management software to make these metrics clear and intelligible with diagrams and charts.
4. Conclusion
There are numerous test metrics available. A successful software testing operation depends on choosing the correct metrics, adhering to them, and taking the necessary steps to enhance them. We offer concise summaries of test data for a variety of factors, including test kinds, organizational usage, waterfall vs. agile, and manual vs. automated testing.
Software testing metrics have a significant positive impact on the testing process. These play a vital part in the software development lifecycle, from assuring the correctness of the various tests carried out by the testers to validating the product’s quality. Therefore, you can improve the efficiency and accuracy of your testing efforts and obtain superior quality by integrating and applying these software testing metrics.
At TestDel, we created a platform that enables agile teams to comprehensively assess the quality of their software. We collect data from all automated and manual testing methods and combine it to produce a single, unified measure of test coverage and software quality rather than concentrating on discrete metrics. Teams are shown the quickest ways to reduce risk in their software projects as well as the level of risk that already exists in those projects.
We’d be pleased to answer any questions you have about testing metrics and how it may benefit your business. Contact us for quality QA and testing services. Let’s raise the bar for your software’s quality!