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Must-Know Code Coverage Metrics

Last Updated: October 31, 2023

Written & Summarized by: Jannik Lindner

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Highlights: The Most Important Code Coverage Metrics

1. Statement Coverage
2. Branch Coverage
3. Function Coverage
4. Condition Coverage
5. Path Coverage
6. Line Coverage
7. Class Coverage
8. Decision Coverage
9. Loop Coverage
10. Entry/Exit Coverage
11. Multicondition Coverage
12. Integration Coverage

Code Coverage Metrics: Our Guide

Dive into our updated report on must-know code coverage metrics, a fundamental resource for software developers and testers across the globe. Keeping an eye on these metrics is integral to ensuring the effectiveness, reliability, and overall quality of your software. Unearth the latest insights and trends to enhance your code quality and boost your software’s performance.

Statement Coverage

Measures the percentage of code statements executed during testing. It helps identify parts of the code that were not executed and can reveal gaps in test scenarios.

Branch Coverage

Evaluates the percentage of branches (e.g, if-else statements) executed during testing, ensuring both true and false conditions are tested.

Function Coverage

Assesses the percentage of program functions or methods that have been called during testing. It helps identify untested portions of the codebase and incomplete test suites.

Condition Coverage

Quantifies the percentage of logical conditions within a program that have been evaluated during testing, ensuring all possible outcomes of each condition are tested.

Path Coverage

Calculates the percentage of unique paths through the code that have been executed during testing. It takes into account all possible sequences of function calls, loops, and branches.

Line Coverage

Analyzes the percentage of lines of code executed during testing. It provides a high-level view of code coverage and helps identify untested or under-tested code.

Class Coverage

Measures the percentage of classes that have been instantiated and exercised during testing. This metric is particularly relevant for object-oriented programming languages.

Decision Coverage

It examines the percentage of decision points (e.g., if statements, case statements) in the code that have been tested, focusing on the decisions made within the application logic.

Loop Coverage

Evaluates the percentage of loops (for, while, do-while) executed during testing. It ensures all iterations and exit conditions of loops are considered in test scenarios.

Entry/Exit Coverage

Assesses the percentage of entry and exit points in a program that have been tested, ensuring that all possible routes into and out of the application logic are exercised.

Multicondition Coverage

Measures the percentage of evaluated combinations of logical conditions during testing, highlighting untested or partially tested compound decisions.

Integration Coverage

Quantifies the percentage of tested interactions between software components, highlighting potential communication issues.

Frequently Asked Questions

What is a code coverage metric?

A code coverage metric is a measurement used to evaluate the quality of software testing by determining the percentage of code executed during testing. It helps to identify untested areas of the code and ensure that all code paths are tested, ultimately improving the software’s overall reliability and functionality.

What are the different types of code coverage metrics?

There are several types of code coverage metrics, including statement coverage, branch coverage, function coverage, and condition coverage. Each type focuses on a different aspect of the code, such as individual lines, decision points, functions or methods, or specific conditions within an expression.

How do code coverage metrics improve software quality?

Code coverage metrics improve software quality by identifying untested or under-tested areas of the code. This information helps developers to focus their testing efforts on these parts and make sure that the whole codebase is thoroughly tested. By ensuring that all code paths are executed during testing, the likelihood of undiscovered errors and vulnerabilities decreases, resulting in more reliable and secure software.

Can having 100% code coverage guarantee a bug-free software?

No, achieving 100% code coverage does not guarantee bug-free software. While it ensures that all code paths have been executed during testing, it does not assess how well the code handles different input values, edge cases, or real-world scenarios. It’s essential to combine high code coverage with other testing methodologies, such as unit testing, integration testing, and acceptance testing, to thoroughly test the software and minimize the risk of bugs.

What tools are commonly used to measure code coverage metrics?

Several tools are available for measuring code coverage metrics, with some tailored to specific programming languages, frameworks, or platforms. Examples of popular code coverage tools include JaCoCo for Java, Istanbul/NYC for JavaScript, Coverage.py for Python, and SimpleCov for Ruby. Most integrated development environments (IDEs) also support code coverage analysis through plugins or built-in tools.

How we write these articles

We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.

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