Test coverage is a popular term used in the software testing industry and a critical indicator of software quality and effectiveness. In fact, test coverage and code quality are two metrics used to track and measure the quality of software products. While both of these metrics are interlinked, they can help in monitoring the software testing practices that affect software quality. For instance, testers might measure code quality by looking at its corresponding test coverage. However, they can see the effectiveness of using test coverage metrics to measure code quality. QA testers use test management tools to ensure maximum test coverage and better results.
We have witnessed many disastrous events due to software glitches around the globe. One such event occurred at London’s Heathrow Airport this year, on February 16th, 2020. Due to a technical fault in the airline’s flight management system, more than 100 flights to and from this airport were disrupted. Passengers were unable to get any information regarding their flights due to a disruption in the departure boards, and check-in systems, limiting their access to using their electronic tickets.
All this happened due to the failure of engineers to conduct a thorough test coverage of all possible real-life scenarios. In this article, we shall discuss different aspects of test coverage and how it affects the production in a software testing project.
How do we define Test Coverage?
Test coverage is a technique that is used to determine whether test cases actually cover an application’s code and how much code is exercised when those test cases are run. Let’s say, there are 10 requirements and 50 tests created and if 40 tests are executed then test coverage will be 90%. Based on this metric, testers can create more test cases for the remaining tests.
Following are some more benefits of test coverage:
- Testers can identify gaps in requirements, test cases, and bugs at an early stage.
- Test coverage analysis can help in preventing defect leakage
- It also helps in regression testing, test suite augmentation, test case prioritization, and test suite minimization.
Test Coverage Metrics
Test coverage metrics are used to measure the testing efforts and address the question: ‘How much of an application was tested?’ Test coverage metrics can be divided into three categories:
- Code-level metrics
- Feature testing metrics
- Application-level metrics
In order to make the most of their software testing efforts, QA testers use test coverage metrics. It helps them in addressing their concerns with respect to quality, as to which areas of an application will ensure success in the market.
Code Level Metrics
This metric follows the test execution coverage percentage method which is also known as the total number of executed tests. It also highlights the percentage of passed/executed tests out of the total number of tests. So, the key formula this metric follows is:
Executed Tests = Number of tests run ÷ Total number of tests to be run × 100
Advantage: A complete overview of testing progress by counting the number of passed and failed tests.
Disadvantage: Counting passed test cases does not ensure its quality. For instance, some tests might only be passed because they checked some errors in the test code, while it does not function as per requirement.
Feature Level Metrics
These metrics are further divided into the following categories:
Requirements Coverage: It is used to discover how well the test cases have covered the software requirements. For this, testers simply need to divide the number of requirements covered by the total number of scoped requirements for a sprint or project.
In the requirements module, testers create test coverage by linking tests to a certain requirement. With test coverage, they can assess the impact of a change in the test or requirement. So here, instead of covering each requirement, testers can cover a certain requirement by test configuration. A specific use-case of a test is known as a test configuration. These test configurations provide better results by covering multiple use cases.
Test Cases by Requirements:
Testers use this metric to see what features of an application are being tested and the number of tests that are associated with a requirement. Most of these requirements contain multiple test cases. It is extremely critical for testers to rewrite the test cases of specific requirements. Thus, this is an important metric for stakeholders as it depicts the progress of an app development project.
Application-Level Metrics
These metrics are further divided into two types:
Defect Density: It is a popular way to measure the total number of known defects divided by the size of the software being measured. This metric can be helpful in identifying areas that require automation. If the defect density for a specific functionality is high, then it needs to be retested. In order to reduce the efforts of retesting, test cases for known defects should be automated.
Thus, when evaluating the defects, it is critical to consider their priority (high or low). For instance, testers may pass multiple low priority defects since the acceptance criteria have been satisfied. While on the other hand, only high priority defects can prevent satisfactory acceptance criteria.
Requirements without Test Coverage
Once you have calculated the requirements coverage, there may be some requirements that are not covered. Thus, it is important to identify each requirement that has not been covered and what stage the requirement is in.
Test engineers and developers use this metric to identify and remove all the uncovered requirements from total requirements before they are sent into the production phase.
Conclusion
Since software development and testing processes have become more systematic nowadays, organizations look for measures to ensure the effectiveness and quality of test completion criteria. Test coverage is a valuable tool to measure software quality.
