Testing and Quality Assurance can be endless tasks. That’s why testing teams need metrics to measure and quantify their work and success. Testing metrics provide tangible ways to measure the progress of testing, as well as the readiness to deploy a product.
One of the most common and useful metrics is code coverage. Many testers consider it a good practice to write test cases that provide maximum code coverage and verify the expected and wanted behavior of the software.
Code coverage helps testers guide the testing by numerically and graphically visualizing the aspects of code that have been tested and the ones that aren’t working correctly. We usually think increasing test cases can only increase that code coverage, but you can increase the coverage percentage of the code by removing unnecessary code.
Let’s dive deeper into code coverage and how you can use it to measure your readiness to deploy.
What is Code Coverage?
Code coverage is a percentage computed by dividing the number of features executed by the number of features in the code. Testing all aspects of code and thereby discovering all faults is highly desirable and yet an unlikely task. Thus, testers select a subset of the input domain and test the software against the expected outcome. These subsets ensure that all the branches of the program are being tested.
Code coverage metrics analyze the control structure of the test object and generate a coverage metric. It identifies the parts of the source that were untouched during dynamic testing.
Generally, code coverage of 80% or above is considered ideal. It is assumed that the higher the test coverage, the lower the defects will be.
Before code coverage analysis is made, a checklist for all the major functions is made to ensure that all those are covered and tested before product release.
Reviewing the software product ensures that correctness, effectiveness, maintainability, and security are guaranteed while developing and testing any software.
You can code coverage percentage using a simple formula:
Code Coverage Percentage = (No. of lines of code executed by testing algorithm / Total no. of lines of code in a system component) * 100
Benefits of Code Coverage
Code coverage is used for validating the test cases that testers run on source code. They do not evaluate the software product or assess whether the source code is bug-free and correct.
Code coverage analysis provides 3 main benefits:
Faster product delivery
Code coverage allows developers to complete software development with efficient testing, faster. Code coverage helps increase the efficiency and productivity of the team. This allows the development team to deliver more products and enables the team to launch more software applications in a limited time frame. It also increases customer satisfaction and generates a high return of investment (ROI).
Bad code is exposed
Code coverage provides continuous analysis and allows developers to identify bad, unused, and dead code. This will enable them to improve their code-writing practices and improve the maintenance of product quality.
Developers must write scalable code to extend the software program using new or modified functionalities. Code coverage can prove to be a useful metric in determining whether the written code is scalable or not. It allows the developers to ensure that code quality is well-maintained throughout the program and that any additional features can be added without much effort.
Shades of Testing and Code Coverage
Broadly, software testing can be classified into three categories: black box, white box, and gray box testing. This categorization is due to the need for testers to ensure that all aspects of the code are covered.
- Black box testing ensures that the software is tested based on the functionalities and external contract.
- White box testing ensures that the software is tested with complete knowledge of the internals.
- Gray box testing can be inferred as either black-box with guesswork about the internals or as white-box constrained by surface-level internal knowledge that can be reasonably expected.
Sometimes people feel that conducting only black-box testing is enough while moving from unit testing towards system testing. However, black-box testing isn’t sufficient to avoid the greater coupling that arises from standard object-oriented design guidelines.
Since black-box testing isn’t sufficient to test all the coding implementations and provide maximum code coverage, white-box and gray-box tests are used. They offer complete functional coverage.
Testing unwanted and unnecessary code is a waste of time and effort, and thus removing it can reduce the testing burden. You can remove the unused code, code that isn’t needed, code that has higher complexity than necessary, and commented code.
Removing redundant tests or not writing them, to begin with, is also good practice. Extra tests don’t add more validation to the software, but they consume time to run and require attention to maintain.
Code Coverage vs. Test Coverage
There is often confusion regarding code coverage and test coverage. Some people consider them the same, and others think they are competing. But in fact, code coverage exists under test coverage.
It means that any testing strategy can be effective if one primarily strives for test coverage which includes code coverage. If you focus on maximum test coverage, the code coverage will naturally follow suit.
Test coverage is measured by the following:
- Mapping actual test cases and requirements
- Checking the status of the test cases
- Code coverage includes code that indirectly or directly implements any requirement.
Many testers consider it a good practice to write test cases that provide maximum code coverage and verify the expected and wanted behavior of the software. This practice, coupled with branch coverage (testing each branch of code), ensures that the testing team has done all they can in ensuring that all the paths in the codebase are covered.
Code coverage helps you plan releases
As we have seen, code coverage has various types, and each has a different purpose. Thus, it is essential that the team is aware of what they want to measure and what purpose the measurement is used for.
For example, if you aim to test the coverage of code execution, you should choose branch or path coverage. If any other type of metric is selected, it will incur significant runtime overhead. It is always beneficial to use code coverage as a metric to write good test cases which can test all the various paths and branches of the codebase.
Developers nowadays work on more strict deadlines. A code coverage analysis report can help them track the portions of code that work well and deliver them to the customer as part of the agile workflow.
Keep in mind that achieving 100% code coverage isn’t feasible. Even if you have completed a high code coverage, this metric alone cannot ensure a market-ready product.
Did you know that coverage is a key feature of Xray, since it gives you the ability to analyze how your issues (e.g. stories, requirements, and even bugs) are in terms of coverage? Take a look at this article to understand how Xray calculates coverage status.