What is Branch Coverage in Unit Testing?

Unit Testing is the process of writing the test cases for each developed code file. This testing is carried out by developers. Once the coding part is done, the developers will write the test cases to cover each scenario in the developed file. While running the test suites or test files, we can see there are four parts of coverages. They are Branch Coverages, Statement Coverages, Functions Coverages, and Condition Coverages. It includes the percentage covered for each coverage category. In this article, we will explore the concept of Branch Coverage in Unit Testing.

What is Branch Coverage in Unit Testing?

Branch coverage in unit testing is a metric that measures the percentage of branches (decision points) in the source code that have been executed during the testing process. It indicates how well the test cases navigate through different possible outcomes of conditional statements, helping evaluate the thoroughness of testing. A high branch coverage means that most decision paths in the code have been tested, increasing the likelihood of detecting potential defects.

1. Achieving comprehensive branch coverage is important for ensuring the reliability and effectiveness of unit tests in identifying and addressing code issues.
2. This metric helps identify areas of code that may not have been adequately tested, increasing the likelihood of detecting potential defects and enhancing the overall reliability and quality of the software.

Formula for Branch Coverage:

Branch Coverage = (Total Number of Branches) ÷ (Number of Executed Branches) ✕ 100%

What is the Purpose of Branch Coverage in Unit Testing?

The main purpose of the Branch Coverage in Unit testing is that the test cases should cover each branch statement inside the coding block or functions block. It is also known as decision coverage.

1. Comprehensive Test Coverage: The primary purpose of Branch Coverage in unit testing is to ensure test coverage by targeting each branch statement within code blocks or functions. This is also known as decision coverage. It aims to verify that every possible branch, including conditional and unconditional statements, has been executed at least once during testing.
2. Conditional Statements Coverage: Branch Coverage addresses the coverage of conditional statements such as if, if…else, while, for, and do…while statements in the source code. The metric includes these conditional statements in the overall branch coverage calculation for the file.
3. Unconditional Statements Coverage: The metric also encompasses the coverage of unconditional statements in the source code, such as printable statements and return statements from functions and methods. These unconditional statements’ coverage contributes to the overall branch coverage for the file.
4. Execution of Branch Test Cases: Branch Coverage involves the execution of test cases specifically designed for branch statements. These test cases are executed, and the results are analyzed to ensure that each branch has been exercised.
5. Execution with Mock Data: To enhance test scenarios, Branch Coverage may utilize mock data or additional data sets when executing branch test cases. This ensures a more thorough examination of the code’s decision paths.

What are Branch Coverage Metrics?

Branch Coverage Metrics serve to gauge the effectiveness of test coverage by measuring how many branches or decision logics within the source code are covered. Typically expressed as a percentage, the branch coverage percentage is a key indicator of testing thoroughness. The evaluation formula is automated within testing software, where, for example, covering 7 out of 10 branch statements results in coverage of (7/10) * 100, yielding 70%. This indicates that 3 branch statements remain uncovered.

Test Coverage Evaluation

1. 70-80% Coverage: If the coverage falls between 70% and 80%, it’s acceptable, but improvements may be needed for overall coverage. This range is often flagged in yellow, signaling a cautionary status.
2. Above 80% Coverage: Achieving coverage above 80% is considered efficient, and it is typically highlighted in green, indicating a robust test coverage.
3. Below 70% Coverage: If coverage is below 70%, it is not recommended, and efforts should be made to increase coverage. This is usually highlighted in red, signaling inadequate test coverage that requires attention.

Example of Branch Coverage in Unit Testing

Consider the following simple function that determines whether a person is eligible to vote based on their age. The function has two conditional statements (if and else) and one unconditional print statement. Branch coverage in unit testing for this function aims to cover all possible branches, including both conditional and unconditional statements.

checkAge(int age) {

if (age >= 18)

print(“Eligible to Vote”)

else

print(“Not Eligible to Vote”)

print(“Print the Age: %d” + age)

}

Branch Coverage Test Cases

1. Test Case 1 (True Positive): Create a test case to cover the if condition by providing an age greater than or equal to 18. This scenario represents a True Positive, where the individual is eligible to vote. This test case covers the if part of the function.
2. Test Case 2 (False Positive): Generate a test case to cover the other condition by providing an age less than 18. This represents a False Positive scenario, where the individual is not eligible to vote. This test case covers the other part of the function.
3. Test Case 3 (Print Statement): Design a test case to cover the print statement by providing mock data for age and verifying that the age is printed correctly. This test case ensures coverage of the common print statement executed in the function.

Tools Used for Branch Coverage in Unit Testing

There are many tools used for branch coverages. Let’s see some of the tools.

1. Coverage.py: An open-source tool designed for the Python programming language, Coverage.py aids in analyzing code coverage for developed Python code. It provides insights into which parts of the code are exercised by unit tests.
2. JCov: An open-source testing tool tailored for the Java programming language, JCov facilitates testing and analysis of code coverage. It allows developers to gain visibility into the branches covered by their unit tests in Java applications.
3. JaCoCo: Another open-source code coverage testing tool, JaCoCo, is widely used for both Kotlin and Java programming environments. It provides detailed insights into code coverage, including branch coverage, to ensure comprehensive testing.
4. CoCo: This testing tool is utilized to analyze code coverage for the C programming language. Unlike some others, CoCo is not an open-source tool but serves the purpose of assessing coverage in C code.
5. Coverlet: An open-source tool designed for the .NET framework, Coverlet enables the analysis of various coverage metrics, including branch coverage. It is particularly useful for assessing the thoroughness of unit tests in .NET applications.

Advantages of Using Branch Coverage in Unit Testing

1. Efficiency: Branch coverage provides an efficient metric for assessing how thoroughly unit tests have explored different decision paths within the code. It ensures that various branches, including conditional and unconditional statements, are executed, contributing to a more comprehensive test suite.
2. Knowledge Gaining: Writing test cases for branch coverage requires a deep understanding of the code, promoting knowledge acquisition. This learning process enhances developers’ familiarity with the codebase and its decision logic.
3. Supports Deployment Activities: Branch coverage supports higher-level deployment activities by ensuring that critical decision points in the code have been adequately tested. This helps in delivering more reliable and robust software.

Disadvantages of Using Branch Coverage in Unit Testing

1. Requires Learning: Utilizing branch coverage in unit testing demands coding knowledge to write effective test cases. This could pose a challenge, particularly for beginners or individuals with limited programming experience.
2. Additional Mock Data Needed: In some cases, creating mock data becomes necessary to execute test cases effectively. This may lead to the addition of dummy files or data in the source code folder, potentially impacting the cleanliness of the codebase.
3. Uncovered Branches: Despite efforts, it may be challenging to cover all branches in the code. Certain branches, especially those involving complex logic or exceptional scenarios, might remain uncovered, limiting the overall effectiveness of branch coverage. Continuous refinement of test cases is essential to address such challenges.

Conclusion

In conclusion, Branch Coverage in Unit Testing is an important metric that evaluates the thoroughness of test cases in navigating decision paths within code. It ensures effective testing of both conditional and unconditional statements, contributing to reliable software. While tools like Coverage.py, JCov, JaCoCo, CoCo, and Coverlet facilitate branch coverage analysis, the approach provides efficiency and knowledge acquisition. However, challenges such as the need for coding knowledge, additional mock data, and potential uncovered branches should be addressed for optimal testing outcomes.