Understanding Code Coverage in Software Testing

Cover Image for Understanding Code Coverage in Software Testing
Arindam Majumder
Table of Contents

Code Coverage is a very important part of the Software Development Life Cycle. It helps to ensure that tests are covering the most critical parts of our application.

In this article, we'll explore what is Code Coverage, Its importance, How it works, and many more!

So Without delaying further, let's Start!

What is Code Coverage?

MacBook Pro showing programming language

Code coverage measures the percentage of code that is tested. It's a very useful metric to test the quality of the test suite. It is one of the forms of White Box Testing.

A program with high test coverage is more likely to have fewer bugs compared to a program with low test coverage

The Formula of Code Coverage:

*Code Coverage Percentage = (Number of lines of code executed)/(Total Number of lines of code in an application) * 100.*

Why Code Coverage Matters?

But Why should we care about Code Coverage? What will we get from it?

In this section, we will explore the importance of Code Coverage:

  • It helps us to find the untested code where the potential can be. So by finding them, we can minimize the chances of getting bugs.

  • It helps detect regressions early. By tracking code coverage over time, any drop in coverage can indicate a regression that requires further testing.

  • By detecting the unused it helps to improve the maintainability.

  • It seamlessly integrates with CI/CD pipelines, automating coverage measurement throughout the development lifecycle.

  • It works with a variety of programming languages and platforms like C#, Java, JavaScript, and more. This allows the testing of heterogeneous systems

How Code Coverage Works?

turned on monitor displaying programming language

There are many approaches to measure Code Coverage but overall there are three main ways to measure it.

  1. Instrumentation - The code coverage tool modifies the source code by inserting tracing calls. This allows it to track the executed parts of the code.

  2. Test Execution - The test suite is run against the instrumented code. The tracing calls collect data on which lines, functions, branches, etc. were executed.

  3. Reporting - The code coverage tool analyzes the collected data and generates a report showing the percentage of various coverage criteria that were met.

Levels of Code Coverage:

Several levels of code coverage can be measured to determine how thoroughly your test suite exercises your code:

  1. Statement coverage - This measures the percentage of statements in your source code that have been executed by tests. Simply counting the number of lines executed gives you the statement coverage.

  2. Branch coverage - This measures the percentage of branches in your source code that have been executed. Branches refer to decision points like if-else statements, loops, etc. To achieve full branch coverage, tests must execute every branch at least once.

  3. Condition coverage - This measures the percentage of conditions within branches that have been tested for both true and false. It focuses on individual boolean expressions rather than full branches.

  4. Path coverage - This measures the percentage of possible paths through your program that have been executed. It takes into account the order of execution and all possible paths through the code.

  5. Modified condition/decision coverage (MC/DC) - This is the strongest form of coverage and requires that each condition independently affects a decision outcome. To achieve MC/DC coverage, tests must vary one condition at a time while holding others constant.

There are several tools available in the market to measure the Code Coverage of any Software. Some are given below:

  • Cobertura

  • Clover

  • Gretel

  • Kalistick

  • JaCoCo

  • JTest

  • OpenCover

  • Emma

  • GCT

Conclusion:

If you found this blog post helpful, please consider sharing it with others who might benefit. You can also follow me for more content on Javascript, React, and other web development topics.

To sponsor my work, please visit: Arindam's Sponsor Page and explore the various sponsorship options.

Connect with me on Twitter, LinkedIn, Youtube and GitHub.

Thank you for Reading 🙂

Author

Taggedcodingdeploymentsoftware-developmenttesting


More Stories

Cover Image for Canary Testing: A Comprehensive Guide for Developers

Canary Testing: A Comprehensive Guide for Developers

Animesh Pathak

Table of Contents What’s Canary Testing, Anyway? Imagine you’re a miner with a canary in a cage. If the air...

Cover Image for Mock vs Stub vs Fake: Understand the difference

Mock vs Stub vs Fake: Understand the difference

Arindam Majumder

Table of Contents Introduction Testing software is like putting it through a series of challenges to make sure it’s tough...