Code coverage refers to the degree to which a particular test suite tests the source code of a program. When a code is executed, the statements and branches are said to be “covered” by that execution. When a test suite covers all statements and branches, we say it has 100% branch or statement coverage.
The basic idea behind code coverage measurement is simple: you run your test suite, and as it runs, the code coverage tool tracks which line in your program were executed by the test suite. The ratio of lines executed to total lines in the file or project becomes the code coverage for that file or project. Code coverage tools generally produce statistics for each file and an entire project. The statistics allow you to see at a glance which files have been thoroughly tested and which have not been tested at all, helping you identify areas that need where more tests.
Measuring code coverage is helpful because it gives you an idea about how well a test suite exercises the source code of a program or — conversely — lets you know how much-untested code there is. It measures how much of the application code is exercised during test execution. Code coverage provides data that helps us prioritize test efforts and gives us insight into testing efficiency.
A common mistake is to think that because you have good test automation because you have high code coverage. However, the other way around is true: you will get high code coverage if you have a good test automation suite. Testers who strive for higher code coverage levels will often get more out of their tests overall. However, to truly reap the benefits of code coverage, you need to first know why it is important and then learn how to optimize your code coverage as much as possible without sacrificing time, resources, and effort.
How is code coverage helpful for testers?
Code coverage is a helpful tool for testers because it helps to ensure that they have tested all the paths through an application, including error conditions. This helps to reduce defects in production and improve the product’s testability. However, code coverage does not replace testing by humans; it gives the tester a better idea of what area of coverage and what areas need more attention.
Testers can benefit from code coverage as it gives them visibility into what parts of the application are not test by the automated test suite. In addition, tests that don’t touch certain parts of the application can indicate areas where bugs could slip through the cracks if we were to add them in the future.
Many things affect code coverage, including how you write them. For example, suppose the development team follows Test Driven Development practices. In that case, there is a high probability that tests will cover 100% of all code because developers will write failing unit tests for each new method or class and then write just enough production code to make them pass. In contrast, if developers don’t write unit tests before writing production code and only write integration tests after the fact there will likely be many gaps in test coverage.
What is the benefit of test coverage?
Using a code coverage tool during your testing cycle can be very beneficial. It can help you determine how much of your application code has been covered by automated tests and which areas have not yet been written or executed.
Checking in new source code without updating your test cases can lead to higher levels of untested code. An intelligent approach to software testing is to look into every single bit of untested code for bugs and defects. When your application’s test cases cover 100% of the application’s source code, this indicates a high level of test coverage, meaning there are no uncovered lines of code in your application. Finding no bugs indicates the application’s quality is relatively high.
How to Implement Code Coverage in Your Application?
There are many open-source and commercial tools available that give code coverage reports, but they differ in their implementation. Some provide reports based on unit tests, while others provide reports based on other tests like integration and acceptance tests. Some tools only show the percentage of code covered during a test case execution, while others provide detailed reports about each class, line of code, and method in detail. Most of the open-source tools work only at the unit level, while most commercial tools work at all levels, including integration and acceptance.
One of the tools that are used for Code coverage is Jacoco. Jacoco is one of the most popular code coverage tools for test automation. It is an open-source tool that has been around for a while and works with a various build, unit testing, and CI systems. Jacoco works on a bytecode level and does not need any additional configuration for your project. It is designed to work with the Ant, Maven, Gradle, and Eclipse plugins but can also be used as a command-line tool or as part of a Jenkins build script.
Jacoco offers tools to measure the following metrics:
- Line coverage: measures how many lines/statements have been executed in your code.
- Branch coverage: measures the execution of each possible branch (if/else, etc.).
- Class coverage: measures how many classes have been executed.
- Method coverage: measures how many methods have been executed.
- Instruction coverage: percentage of all Java bytecode instructions executed during the test run.
How to interpret and analyze code coverage reports?
When we look at a coverage report, we should ask ourselves these questions:
2) Are there parts of my application running but don’t do anything? Again, This is an excellent place to start looking for dead code.
3) How many of the lines that did run contained conditionals? Do I have enough tests to cover all options?
4) If a line calls another method, do I have enough tests to cover all possible outcomes?
5) Is there anything more I can do to make my software better tested without sacrificing maintainability or design quality?
6) Are there proper unit test cases in place that ensure to cover all the error codes and negative scenarios?
Working towards these questions can improve the code coverage percentages to give a better report.
Code coverage does not measure quality. It doesn’t measure the number of bugs in your software. It doesn’t measure whether you have good or bad tests. Code Coverage also doesn’t even tell you whether your application works in production or not. Achieving 100% code coverage with good tests can still leave you with bugs, while achieving 40% code coverage with poor tests will likely result in a higher bug density.
It’s important to understand that 100% code coverage is not a goal itself; it’s just a tool to get to the purpose of achieving higher quality. The goal should always be better quality software — and code coverage is one tool to help us achieve that goal.