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What Is Code Coverage?
Code coverage measures how many lines, statements, or blocks of your code have been tested using automated tests. This metric helps you understand the quality of your QA efforts. Code coverage shows how much of your application is vulnerable to defects. It is measured in percentage, and the closeness to 100% seems good, however, it’s not mandatory. Currently, many code coverage tools are available. Be it open-source or commercial, these tools have the ability to serve the code coverage needs of any software development project. These tools are helpful in every kind of need - private or enterprise. A code coverage tool works with a specific programming language. Apart from that, they can be integrated with:
- Build tools like Ant, Maven, and Gradle.
- CI tools like Jenkins.
- Project management tools like Jira, and more.
Benefits of Code Coverage:
- Code Maintenance: To make a code scalable, you need to extend the software program by introducing new or modified functionalities, but there isn’t any way to know whether the code is scalable. Hence, code coverage comes into the picture, as it is a useful metric, and after seeing the analysis report, developers can ensure if the code quality is well-maintained or new features can be added easily.
- Bad Code Discovery: Continuous monitoring helps developers to understand bad, dead, and unused code. And with the help of segmentation, they can improve code-writing practices, which will result in better maintainability of the product quality.
- Faster Product Building: This metric helps developers to finish the software development process faster, thereby increasing their productivity and efficiency. Hence, they can deliver more products, allowing companies to launch more software applications into the market in a shorter time. Apart from improved productivity, it leads to increased customer satisfaction and high ROI.
Code Coverage Criteria
To measure the lines of code using test runs, teams follow a specific criterion. You can see a few critical coverage criteria that companies use:
- Function Coverage: The functions that are called and executed at least once in the source code.
- Statement Coverage: The number of successfully validated statements in the source code.
- Path Coverage: The flows containing a sequence of controls and conditions that have worked well at least once.
- Branch or Decision Coverage: The decision control structures that have executed fine.
- Condition Coverage: The validated Boolean expressions that execute both TRUE and FALSE as per the test runs.
Best Code Coverage Tools
Code coverage is essential for many reasons, hence, selecting the right tool for an organization is crucial. Below, you will find the list of code coverage tools that you can use in your organization.
- Default Visual Studio Code Coverage:
Visual Studio Enterprise provides Code Coverage as an inbuilt feature. This code coverage can be applied to both managed (CLI) and unmanaged (native) code. However, it is provided as an option, and you try it using Test Explorer. To use it, you can select all of your tests and their subsets. It helps you visualize the coverage while you look at the code. Besides this, it helps you see the results that show the percentage of the code run in each assembly, class, and method.
- Cobertura:
It is one of the most used and widely known code coverage tools. It is popular for being a free Java tool that calculates the percentage of code accessed by various tests performed. It can be used to identify which parts of your Java program are lacking test coverage. To execute code coverage using Cobertura, you need Ant, Command-Line, Eclipse plugin, or Maven plugin. The best part of Cobertura is that it is easy to use and can measure coverage without having the source code. It can test lines and branches of class and method and provides reports in HTML or XML format.
- Coverage.py:
It is a code coverage tool built for Python. Mainly, Coverage.py is used in the execution, analysis, and reporting phases. This tool monitors the Python programs and after monitoring the parts of the code that have been executed, it analyzes the source to identify code that could have been executed but couldn’t. You can execute code coverage using Coverage.py via command line, APIs, as well as Plug-ins for Django and Mako.
- SimpleCov:
SimpleCov is a code coverage analysis tool for Ruby. It uses Ruby's built-in Coverage library to gather code coverage data, but makes processing its results much easier by providing a clean API to filter, group, merge, format, and display those results, giving you a complete code coverage suite that can be set up with just a couple lines of code. In most cases, you'll want overall coverage results for your projects, including all types of tests, etc. SimpleCov automatically takes care of this by caching and merging results when generating reports, so your report actually includes coverage across your test suites and thereby gives you a better picture of blank spots.
- Istanbul:
Istanbul is a test coverage tool that works with many different frameworks. It tracks the parts of your code that are executed by your unit tests. Thus, you can use Istanbul to view and see coverage gaps or integrate it into your CI pipeline to enforce coverage levels. Ultimately, Istanbul enables data-driven testing. The NYC package Istanbul’s CLI tool makes integrating with Istanbul easier no matter the tool you use.
Conclusion
In DevOps or Agile, the emphasis is on building applications faster. However, it is not achievable if your developers and testers are not concerned about the code quality. A bug-free, clean code helps you meet the client’s expectations. On the other hand, a poorly written code complicates the process. Code coverage helps you differentiate between a good or bad code and encourages your team to improve the code’s quality.
However, apart from focusing on the code coverage only, you should give equal attention to the other QA efforts as well.
