Code coverage how does it work

Developing enterprise-grade software products is the ultimate goal of any software company. However, to accomplish this goal, companies have to ensure that the software they develop meets all the essential quality characteristics — correctness, reliability, effectiveness, security, and maintainability. This can only be possible by thoroughly reviewing the software product.

Along with handing off the software to the QA engineers for bug tracking, it is imperative to analyze, monitor, measure test activities. Code coverage is one such software testing metric that can help in assessing the test performance and quality aspects of any software.

Such an insight will equally be beneficial to the development and QA team. For developers, this metric can help in dead code detection and elimination. On the other hand, for QA, it can help to check missed or uncovered test cases. They can track the health status and quality of the source code while paying more heed to the uncaptured parts of the code.

Code coverage analysis can only be used for the validation of test cases that are run on the source code and not for the evaluation of the software product.

Also, it neither evaluates whether the source code is bug-free nor proves if a written code is correct. To measure the lines of code that are actually exercised by test runs, various criteria are taken into consideration. We have outlined below a few critical coverage criteria that companies use.

As tests are developed, code coverage highlights aspects of the code which may not be adequately tested and which require additional testing.

This loop will continue until coverage meets some specified target. It is well understood that unit testing improves the quality and predictability of your software releases.

Do you know, however, how well your unit tests actually test your code? How many tests are enough? Do you need more tests? These are the questions code coverage measurement seeks to answer. Coverage measurement also helps to avoid test entropy. As your code goes through multiple release cycles, there can be a tendency for unit tests to atrophy.

As new code is added, it may not meet the same testing standards you put in place when the project was first released. Measuring code coverage can keep your testing up to the standards you require. You can be confident that when you go into production there will be minimal problems because you know the code not only passes its tests but that it is well tested.

Code coverage is not a panacea. Coverage generally follows an rule. Increasing coverage values becomes difficult, with new tests delivering less and less incrementally. If you follow defensive programming principles, where failure conditions are often checked at many levels in your software, some code can be very difficult to reach with practical levels of testing. A simple example Let's say you're writing a JavaScript program: 1. To understand code coverage, it's useful to think about types of lines: Syntax lines are those which don't do anything and can't contain bugs - 1, 7, 8, and Logic lines are those which contain actual code statements - 2, 4, 6, 9 Branch lines are those which change what is executing - things like function calls, if statements, and loops - 3, 5 Code coverage for this program would be the ratio of non-syntax lines which are executed by tests over the total number of non-syntax lines.

Branch Coverage A very relevant concept to code coverage is "branch coverage" - instead of measuring how many lines of code, it measures groups of lines. When to care about code coverage We've already discussed one scenario when code coverage is important: When you've inherited an existing code base. In general, you should measure and optimize for code coverage if any of the following are true: Your product has users, and those users might leave if they are affected by bugs.

You are working with developers that aren't immediately trustworthy like contractors or interns. You are working on a very large code base with many individually testable components - here code coverage analysis can compliment Test Driven Development as a project management tool. Common policies related to code coverage Code coverage is useful as a metric, but it's especially useful if combined with policies and automation as a check that code will not cause bugs for end users.

By Priya Pedamkar. Statement coverage, toggle coverage, branch coverage, decision coverage, and FSM coverage are the methods used by the code coverage. It also allows developers to create extra test case to increase the code coverage. There actually exist various approaches to the measurement of code coverage.

However, if we consider in a broad way, there exist 3 of the approaches that are as follows:. Since the code gets executed under the test, the code coverage system would be collecting information as to which of the statements are executed.