What is Code Coverage in Software Testing
Code coverage metrics determine how much a software application code is being tested. It’s an indication of how thoroughly the code has been tested and evaluated for errors. When a programmer tests their program, developer/testers usually run a test on every single line of code to make sure that it works as expected and record which parts have been covered, uncovered, and then generate a report.
Developers need to be aware of this metric to know if they inadvertently leave any part of their code untested. It’s also important for testers who can use it as another measure when evaluating software testing quality.
- Function Coverage
- Line Code Coverage,
- Branch/Decision Coverage
- Condition Coverage
- MC/DC Modified Condition/Decision Coverage
- Multiple Condition Code Coverage
How Code Coverage Works
Code coverage reports show how much of the code base has been tested. Coverage can be calculated based on a percentage of lines or by calculating the percentage of statements executed. Or by calculating the percentage of branches or multiple conditions executed.
Coverage reports provide information on both the quality and effectiveness of software testing. The main objective is to have all areas covered. This will ensure you have as many bugs as possible caught before they make it into production.
Achieving good (85%) Code Coverage provides peace of mind and helps keep developers/ testers focused and motivated on their work through verification and validation.
Code Coverage Tool can generate Code Coverage reports.
Code coverage tools adds additional code to the source code(instrumentation) during compilation or build and capture the parts of code executed during the test. The percentage of code covered is reported.
When should you run Unit tests or Functional tests?
Unit tests/Functional tests should be run as often as possible, ideally when changes to your codebase and before deployment into the production environment. Unit tests/Functional tests should be run after branches are merged and before release into production. Ideally, unit tests can run continuously on every change made to the software repo or any git/repository branch.
Test Coverage Metrics: Test Coverage metrics can be divided into three categories
Code-level metrics: It measures how many lines in the application under test are executed as part of a test’s execution.
Tests executed= Number of tests run/Total number of tests to be run x 100%
You can check the progress of testing by looking at the number of passed and failed tests. One disadvantage is that it does not say anything about the quality of those tests. For example, some tests might pass because it checks a trivial condition or an error in the test code, but they are not functioning as intended.
Feature testing metrics:
Requirement Code Coverage: Test Coverage effectively measures how much tests cover the software requirements. This is calculated by dividing the requirements covered by all automated tests scoped for a sprint, release, or project.
Requirement coverage=Number of requirements covered/What type of tests were written for them in total
You can create test coverage by linking tests to a requirement. Test coverage examines how the change will impact the original requirement. It is much more efficient and helpful if you cover each requirement at a test configuration level. By doing this, instead of covering one requirement for a test at the test level, you can cover many requirements for that one test by outlining different types of use-cases. This allows your testing to be highly granular and precise.
Test cases by the requirements: This metric is used to identify the features that have been tested, the number of cases where they matched a requirement, and more information. Most requirements include a few tests cases. To know which requirements to focus on, it is crucial to identify which test cases are failing. If you find any test cases failing by the exact requirements, test each requirement separately from one another and figure out why they’re not working.
It is an essential metric for stakeholders. It shows how much progress our application development has made.
Test-Driven Development test coverage (TDD process): Testing the code against expected output is a good idea: The software development process relies on software requirements being turned into test cases. It also tracks all changes to the source code by repeatedly testing it against its original requirements.
How to improve the Code Coverage?
The following approach should help you to improve the code coverage with good tests.
Remove Dead Code: Total coverage measures how much of your code is being covered by tests. The easiest way to increase coverage is to decrease the denominator or untested parts of code. There are two strategies for achieving this: remove Dead Code. Dead code is typically found in the history of a program where both new feature code and debugging code have been added and deleted. You can increase your coverage ratio without adding any extra lines of code.
Before dead code removal, you need to test it for application functionality with code coverage. If it works as expected, then you can remove the unused parts. Some static analysis tools can tell you which parts of your code aren’t being used at static analysis time, which can help clean up the source code. However, the effectiveness of these programs varies from environment to environment, so you should test it out first.
Remove redundant code: A high code coverage ratio (a measure of how well the program’s tests exercise all parts of the software) may be increased by removing redundant code.
The extended program contains duplicated code which is a waste of space & time and can block other parts of the application from working correctly. Removing this code will shrink the program size and allow for additional tests to go smoothly without interference. You can increase your code coverage % by removing the redundant code from your source code.
Functional testing of the application in a live environment: Functional testing can help identify bugs in an application. It is the last step before a release. It is used to ensure that the software performs its intended function in a real-world environment and check for overall code coverage and unexpected crashes while doing functional testing.
For example, testing signal messaging apps in different mobile devices as per requirements and checking for code coverage and unexpected crashes or behavior.
How Do I Know If My Source Code is Covered Enough?
There are two rules for knowing when you should stop testing a program. Rule 1 is allocating enough time to do testing on your own during the development process, and Rule 2 is if the time allocated to testing matches the size of all program modules being tested at 100%. The second approach is to stop testing when the test suite does not detect new errors. However, how do you know if all the parts of the code have been covered with test cases?
When it comes to testing code, you need to be sure every part of the code is covered. But the idea of “code coverage” criteria is that bugs in untested snippets of code can never be detected. Add more test cases until you ensure that all parts of the source code are covered, so you have peace of mind.
In addition to Unit tests, functional tests are a crucial component of software development. They help developers identify bugs in code and ensure that the code will function as intended. The number of tests necessary for testing a particular system depends on the size and complexity of the system. It is recommended that developers do unit and functional tests on any new code they put into their systems. After making changes, do another round of testing to make sure everything still works.
Conclusion: Why You Should Care About Code Coverage?
Code coverage is the measure of how thoroughly your test suite exercises code. The higher the code coverage, the fewer defects you’ll have.
Many tools allow you to analyze and monitor code coverage, such as the open-source has its own pit pitfalls, and commercial code coverage tools like the RKTracer tool have many benefits. You can use these tools to get an overview of your codebase and find out where there might be a problem with design, architecture, or functionality.
Additionally, it is essential to remember that just because your tests cover 100% of your source code doesn’t mean they’re good tests!