Code and test coverage metrics play a crucial role in assessing the quality of your automated tests. Whether you’re working on a legacy application or developing a new feature, measuring the coverage of your tests gives insight into how thoroughly your code is being tested.
Code and test coverage metrics are often misunderstood or conflated, but understanding the difference between them and how to leverage them will ensure that your application is robust and less prone to bugs.
In this guide, we’ll explore code coverage and test coverage, their importance, key differences, best practices for achieving high levels of coverage, and how solutions like Diffblue Cover can automate this process to ensure high-quality results.
What Is Code Coverage?
Code coverage is a quantitative metric used to measure the percentage of our source code executed by automated tests. As a white-box testing approach, it focuses on the internal structure of the code, making it especially useful in unit testing. Code coverage helps developers identify untested parts of their codebase.
A higher code coverage percentage generally boosts confidence that your code is thoroughly tested. However, high coverage alone doesn’t guarantee quality; the quality and relevance of the tests themselves are equally important. By pinpointing untested areas, code coverage guides developers in creating additional test cases, helps identify dead code, and ultimately supports a more robust and well-maintained codebase.
Types of Code Coverage
There are several types of code coverage, each measuring different aspects of your code’s execution. The most common are:
- Line coverage: Measures the percentage of lines executed by the test cases. For example, if your test suite executes 80 out of 100 lines, you have 80% line coverage.
- Branch coverage: Measures the percentage of decision-making branches (i.e., if, else, switch statements) that are executed. It ensures that all logical paths are tested.
Method coverage: Measures the percentage of methods that are invoked during testing. It helps ensure that tests are at least triggering every method in your code.
How to Measure Code Coverage in Java
In Java, you can use tools like JaCoCo or Cobertura to measure code coverage. These tools integrate with build tools like Maven or Gradle, and provide detailed reports on which parts of your code were executed during the test run.
JaCoCo is the de facto standard tool, as Cobertura hasn’t received any updates since 2015. That’s why we focus on a simple example of how to measure coverage using JaCoCo in a Maven project:
Step 1: Add the JaCoCo plugin to your pom.xml
Step 2: run your tests with
mvn test
Step 3: Open the generated HTML in target/site/jacoco/index.html report showing which lines and branches of your code were covered by the tests:
Furthermore, JaCoCo generates a jacoco.exec file in the target folder that can be used in various CI pipelines to integrate the code coverage information with a pipeline overview.
What is Test Coverage?
Test coverage is a qualitative measure that assesses how thoroughly the software’s functionality and requirements are tested. Put another way, test coverage measures how well your tests cover different scenarios or requirements.
It’s a broader term that goes beyond the simple execution of code. Test coverage examines whether your tests check for all possible conditions, edge cases, and functionalities, ensuring that your application behaves as expected under various circumstances.
Types of Test Coverage
Several types of test coverage are important to consider, including:
- Functional coverage: Ensures that all functions of the application are covered by tests, meaning the tests validate that the system meets its functional requirements.
- Scenario-based coverage: Tests the system’s behavior under different use-case scenarios. It helps identify if all business-critical workflows are thoroughly tested.
- Edge-case coverage: Ensures that unusual or boundary conditions (e.g., empty inputs, negative numbers) are tested. These cases often uncover unexpected bugs.
How to Measure Test Coverage
Unlike code coverage, test coverage is not something that a tool can measure directly. It requires defining a set of criteria or scenarios that your system should handle and then ensuring that these are tested.
One effective way to increase test coverage is to use a requirements-based testing approach to map your tests to specific requirements or user stories.
Code Coverage vs. Test Coverage
While both metrics aim to quantify testing quality, they measure different aspects of your tests. Understanding the key differences will help you focus your testing efforts effectively.
Key Differences
- Code coverage: Focuses on how much of your codebase is executed by your test suite. It answers the question: “Did my tests run this part of the code?”
- Test coverage: Focuses on how much of the system’s functionality and scenarios are covered by tests. It answers the question: “Did my tests cover all business logic and scenarios?”
Test coverage is more of a qualitative measure that evaluates the comprehensiveness of your testing strategy. Code coverage is a quantitative metric that can be calculated deterministically based on your tests.
Common Misconceptions
1/ High Code Coverage Doesn’t Guarantee Thorough Testing
To put it in Goohart’s law, “When a measure becomes a target, it ceases to be a good measure.”
One of the biggest misconceptions is that high code coverage equals effective and thorough testing. Code coverage simply measures the percentage of your code executed by tests—it doesn’t necessarily mean that the tests are checking for the correct behavior or ensuring the quality of your code. You can have 90% or even 100% code coverage, but if your tests aren’t validating expected outcomes, edge cases, and error conditions, you could still end up with a buggy application.
There are teams whose main motivation to write tests is to increase code coverage, as the business overemphasizes and tracks this metric. As engineering leader Ed Burns emphasizes, this gives a false sense of security and incentivizes developers to raise the bar without focusing on test quality.
2/ 100% Code Coverage Isn’t Always Necessary or Cost-Effective
Many teams aim for 100% code coverage, thinking it’s the gold standard of software quality. However, striving for 100% coverage isn’t always necessary, and in some cases, it can be impractical or even counterproductive.
In most real-world projects, hitting 100% code coverage involves testing areas of the code that may not need direct testing, such as getter and setter methods, trivial utility functions, or generated code. Testing such trivial or low-impact code might inflate your coverage numbers but does little to improve the overall quality or reliability of your software.
3/ Test Coverage Can Be High Even with Lower Code Coverage
It’s a common misconception that test coverage and code coverage must rise together—that high test coverage is only achievable if you also have high code coverage. However, this isn’t necessarily the case. You can have thorough test coverage even if your code coverage is lower, and vice versa.
Test coverage focuses on the breadth of testing from a requirements or functionality perspective. You might be covering a wide range of functional scenarios and edge cases without necessarily executing every single line of code. This typically happens when you write tests that prioritize critical workflows, user interactions, or edge cases while intentionally skipping over trivial code paths that don’t require detailed testing.
Choosing between Code Coverage and Test Coverage
Teams should generally focus on both metrics to ensure an effective test strategy.
Code coverage is essential when you’re trying to ensure that all areas of your code have been executed during tests. It’s beneficial in legacy codebases where you want to introduce automated tests without refactoring too much. It’s an important metric to keep track of but not a reliable indicator for test quality and likelihood of bugs.
Teams should focus on test coverage and code coverage when validating critical business logic, high-risk areas, and complex systems, where ensuring correct functionality is more important than merely executing code. Test coverage is essential during product releases to confirm that all scenarios and user workflows behave as expected. It’s especially crucial for safety-critical applications or industries with regulatory requirements, where thorough testing of all conditions is mandated.
Different project goals and the nature of the software often determine the emphasis on each type of coverage. For instance:
- Project goals: Use code coverage to ensure that all parts of the code are tested, and test coverage to verify that functional requirements are met.
- Software nature: Apply code coverage in complex systems with intricate logic, and prioritize test coverage in regulatory or safety-critical domains.
- Development process: Code coverage aligns well with Test-Driven Development (TDD) practices, whereas test coverage complements Behavior-Driven Development (BDD) by focusing on behavior and user requirements.
- Stakeholder priorities: Emphasize test coverage for functional correctness and code coverage to manage and reduce technical debt.
Together, code and test coverage give a complete picture of the system’s robustness, balancing both code execution and requirement validation.
Best Practices for Achieving High Code Coverage
Here are a few strategies to ensure you cover as much of your code as possible.
- Write effective unit tests: Unit tests are the building blocks of your test suite. Ensure that every function and method is tested individually with various inputs and edge cases. When working with untested legacy code, teams may align on required tests for newly submitted code.
- Identify untested code: Use coverage reports from tools like JaCoCo to identify sections of code that aren’t covered. Prioritize writing tests for these areas.
- Practice TDD: When implementing a new feature, start with the tests. This will help you focus more on the existence of meaningful tests.
Best Practices for Achieving Effective Test Coverage
While increasing code coverage is important, achieving effective test coverage requires a strategic approach. Here’s how you can enhance your test coverage:
- Prioritize critical code paths: Focus on the most essential parts of your application, such as business-critical workflows, error-handling routines, and complex algorithms.
- Employ mutation testing: Mutation testing involves modifying your code in small ways and ensuring that your tests catch these changes. It helps to identify the quality of your tests, making sure they are reliable.
- Analyze incidents or outages: As part of an incident post-mortem, teams can continuously identify gaps in their test suite.
Leveraging Diffblue Cover for Code and Test Coverage
Diffblue Cover is an AI agent solution that can significantly enhance both code and test coverage by automating the creation of Java unit tests.
Diffblue Cover analyzes your codebase and uses reinforcement learning to automatically generate unit tests for your Java methods, which helps increase code coverage without manual intervention.
By identifying untested parts of the code, it writes meaningful tests that execute previously untested lines, methods, and branches. This helps boost code coverage, particularly in areas where writing tests might be tedious or overlooked.
Diffblue Cover is available as an IDE plugin and a CLI tool.
Let’s see this in action by generating tests for the following method from the OwnerController of the Spring PetClinic project:
With the click of a button inside our IDE, Diffblue Cover automatically detects all execution paths and generates the following two tests for us:
These two tests include the necessary setup methods as well as the relevant assertions for each test. No manual interaction was required to generate them.
Diffblue Cover is particularly useful in identifying and covering edge cases and more complex areas of the code, which might otherwise go untested. This results in better code coverage and more robust and thorough test coverage, as these complex scenarios often contain the most critical bugs.
On top of Diffblue Cover’s unit test generation, Diffblue Cover Reports is a powerful tool for visualizing test coverage across a Java codebase, offering detailed metrics and insights to enhance code quality and testing efforts.
Diffblue Cover Reports is designed to:
- Visualize the state of unit testing in Java projects
- Pinpoint actionable insights to improve code quality and efficiency
- Monitor and manage test coverage
- Identify risks across the organization’s codebase
With browser-based UI reports, teams can easily monitor coverage risks and identify testability gaps:
By leveraging data generated through the Cover CLI or Cover Pipeline, Diffblue Cover Reports delivers unique, actionable insights beyond standard coverage statistics, guiding teams toward a more resilient and well-tested codebase.
Summary
Both code and test coverage are essential metrics for measuring the quality of your tests. While code coverage focuses on ensuring all parts of the code are executed, test coverage ensures that all functional requirements and scenarios are tested.
While you can not over-emphasize the importance of test coverage, taking code coverage as a central quality KPI will leave you with blind spots in your test suite.
Prioritize effective unit tests, employ best practices like mutation testing, and leverage automation tools to maintain high coverage and continuously improve your test suite.
Diffblue Cover can help you with that by automating the tedious part of writing and maintaining unit tests.
Diffblue Cover Reports provides a comprehensive view of test coverage, highlighting coverage gaps, testability issues, and actionable insights to improve code quality and testing efficiency for your project.
Ready to improve your testing process? Start your Diffblue Cover trial today!
