Code Coverage in Software Testing: Techniques, Tools, and Examples

OVERVIEW

What is code coverage? Code coverage is a crucial aspect of the Software Development Life Cycle, which ensures the accuracy and reliability of the codebase of the software application. This gives deep insight and ideas into the effectiveness of the testing efforts and finds the area which requires more focus to improve the functionality of the developed application.

When we consider the need to have quality software applications in this growing competitive software industry, we all know that software testing is one of the approaches to identify bugs, vulnerabilities, and areas of improvement.

However, its effectiveness may get affected, and without any appropriate QA metrics, it is very challenging to assess the functionality of the software applications. Here comes code coverage which determines the extent to which the source code is covered during testing.

It gives comprehensive insights into test suites by identifying tested or untested code of software applications. When you analyze the coverage metrics, you can find the reliability of the testing efforts and make relevant decisions regarding allocating resources for further test enhancement.

What is Code Coverage?

Code coverage is all about validating the number of lines of codes executed under a test process. This, in turn, helps in analyzing how well and comprehensively a software application is being tested. In other words, it is the quantitative measurement of the percentage or degree of executed code (source code of software application) during testing, enabling the team to gauge the completeness of their test cases more accurately.

Code coverage is considered one of the forms of white box testing. This shows that to measure it, the developers and testers need access to the internal structure of the code and its implementation. By having this access, they can identify components of the software application that have not been executed or tested by a specific set of test cases. This information allows them to generate additional test cases that will enhance code coverage and provide a quantitative measurement of its extent.

In most cases, the code coverage tool collects information on applications in their running state. It further compiles that information with source code to develop a report on the test suite’s coverage. This gives information on the extent or degree to which the application’s code has been covered during test execution.

Here are some crucial aspects of code coverage to have more understanding:

• It calculates different types of QA metrics like line coverage, path coverage, and others.
• The coverage is measured at different levels of granularity, like branches, functions, and others.
• It also provides a visual representation like color-coded code that gives information on coverage in a more intuitive and comprehensible manner.
• It can be performed with tools that integrate with popular automation testing frameworks like JUnit, NUnit, and pytest that ease the measurement of code coverage during automation testing.

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Why perform Code Coverage?

Performing code coverage is important, especially during unit testing. Unit tests are typically created by developers who understand the testing process and know which tests to execute. In this process, coverage helps to answer the following questions:

• Are there enough tests in the unit test suite?
• Do additional tests need to be added for better coverage?

Here are some reasons why performing code coverage is important:

• It helps you determine enough tests in the unit test suite. If coverage is inadequate, more tests must be added to ensure comprehensive testing.
• With the progress of software application development, new features and fixes are incorporated into the codebase. However, the test code should be updated when you make changes. With code coverage, you can confirm that testing standards implemented at the beginning of the software project are maintained throughout the Software Development Life Cycle.
• Coverage with a high percentage indicates fewer chances of unidentified bugs in the software application. When you perform testing in production, it is recommended to set a minimum rate of coverage which should be accomplished. This helps lower the chance of bugs being detected post-development of software applications.
• Scaling any software application requires you to write scalable code by adding new or modified functionalities. However, how will it determine the scalability of the written code? The answer is code coverage. With this analysis, you ensure the quality of the code and easily introduce new changes.

Advantages of Code Coverage

Code coverage offers several benefits that show its significance in software testing and testing the quality of the software application. Some of those are explained below pointers:

• It provides developers with quantitative metrics, enabling them to assess the nature and health of their code more effectively.
• If the existing test cases do not provide sufficient coverage, code coverage allows developers to introduce additional tests to ensure comprehensive and robust coverage.
• It helps identify parts of the codebase that have not been exercised during testing or contain dead or unnecessary code. This makes removing such code and errors easier, improving codebase efficiency.
• It helps developers identify untested or poorly tested code, which can be potential sources of bugs.
• It provides a feedback loop for developers, allowing them to evaluate the effectiveness of their test strategies and make informed decisions about test case improvements.
• It can enhance code maintainability by encouraging developers to write modular, testable code and enabling easier identification of redundant or dead code.

Levels of Code Coverage

Code coverage is a way to measure how well testing covers the code. It helps identify areas of our code that may not be tested adequately. There are different levels of coverage that we can consider.

• Branch coverage: This level of coverage provides that every possible branch in the code’s decision-making process is executed during testing. For example, if we have an if-else statement or a loop, branch coverage ensures that both the true and false branches are tested. With this, we are inclined to test all the branches to ensure the expected behavior of the code in diverse conditions.
• Function Coverage: This intends to test all the functions of the code during the Software Testing Life Cycle. Here, each function is invoked, and its different input parameters are tested. This helps us verify whether the logic inside them is working accurately.
• Statement Coverage: This level of coverage ensures that every statement in the code is executed at least once during testing. It includes testing corner cases or boundary cases to ensure thorough coverage. With this, it is possible to ensure that there are no unreachable code paths.
• Loop Coverage: In this level, every loop of the source code is executed at least once. But why is this crucial? It is important because they execute different behavior based on different conditions experienced in run time. Hence, when you test the loop, it is possible to validate its functionality in different scenarios and discover any issue in terms of loop termination.
• Condition Coverage: It verifies how variables in the conditions statement can be analyzed. With this, you can check all the outcomes of the above conditions are tested, including both true and false conditions. When we test diverse combinations of conditions, it is easy to find potential errors or defects due to evaluating these conditions.
• Finite State Machine Coverage: It is one of the most complex levels of code coverage that addresses the structure and design of the application, which may be like a state machine. This coverage measures the frequency of the visits of different static states and transactions within the application. With this, you ensure that the software application has correct behavior in all possible scenarios.

Instrumentation Types in Code Coverage

To measure code coverage, we employ a method known as instrumentation. Instrumentation includes adding extra code or changes to software applications. These additions monitor how the software application runs, gather data, and offer insights into which parts of the code were executed during testing.

This data allows you to evaluate the effectiveness of testing and identify areas that might need additional attention. The types of instrumentation are discussed below.

Code Instrumentation

In simple terms, code instrumentation is the addition of extra statements and instructions to the source code. Such instrumented statements aim to gather data or check for the behavior of the applications during the run time. When you insert the additional statement, the source code is compiled for a specific toolchain for a particular programming language. Such a compilation process helps in integrating the instrumentation statement into the code. This enables the running of instrumented code, facilitating the collecting and monitoring of desired data through the inserted instrumented statement.

Runtime Instrumentation

Runtime instrumentation involves inserting specific statements within the code to collect information from the runtime environment while the code is executing. The capabilities provided by the underlying runtime system, tool, or framework drive this process.

When the application runs, the instrumentation statements gather data, offer insights into code behavior and functionality, and monitor performance. Therefore, runtime instrumentation allows for dynamic analysis of the application's execution characteristics. It further provides real-time data without the need for recompilation.

Intermediate Code Instrumentation

It is applicable in programming environments that utilize bytecode. In this approach, you add an instrumented statement to the compiled class files, which has a bytecode representation of the source code.

In other words, an instrumented class is created by adding byte codes to the compiled class files. These class files contain additional bytecodes covering desired data and application behavior during execution. The instrumented bytecode can then be executed on a virtual machine or interpreter, which interprets and executes the instrumented instructions during runtime.

These different types of instrumentation involve adding statements or instructions to the code to collect data, monitor behavior, or provide insights.

How to Measure Code Coverage?

Code coverage evaluates the extent to which a software's source code has undergone testing and execution in the testing process. It provides insight into the efficacy of the tests by indicating which portions of the code have been exercised and which have not.

The code coverage percentage can be determined using a simple formula. Firstly, identify the number of lines of code executed by the testing algorithm. Then, divide this count by the total number of lines of code in the specific software application component. Finally, multiply the result by 100 to obtain the code coverage percentage.

Code Coverage Percentage = (Number of lines of code executed by a testing algorithm/Total number of lines of code in a system component) * 100.

Criteria for Code Coverage

Numerous criteria exist for measuring coverage for your code, and the subsequent five are frequently used:

• Function Coverage: This examines the functions within the source code that have been invoked and executed at least once during testing.
• Statement Coverage: It measures the number of statements in the source code that have been successfully validated by the tests. Each line of code containing a statement is taken into consideration.
• Path Coverage: This criterion centers on the various paths or sequences of controls and conditions within the code that have been executed at least once during testing. It verifies whether all possible flows have been exercised.
• Branch or Decision Coverage: It looks at the decision control structures, such as loops or conditional statements, to ensure that all possible branches have been executed at least once during testing.
• Condition Coverage: This criterion focuses on the Boolean expressions within the code that are validated during testing. It verifies whether the true and false conditions of these expressions have been tested.

By taking these coverage criteria into account, companies engaged in software development can evaluate the thoroughness of their code testing efforts and identify areas that may necessitate additional testing to enhance code quality and reliability.

Code Coverage Measurement with an Example

Suppose you are performing tests on a software application that consists of 100 lines of code. Out of those, only 50 lines of code are actually validated during the testing process. In this case, the coverage percentage for the software application would be 50 percent.

Reviewing this example, one might assume that achieving 100 percent coverage is the ultimate goal for ensuring high code quality in any software program. However, this assumption is not entirely accurate. So, what is the ideal coverage percentage that developers and testers should strive for? Let us understand this.

Ideal Code Coverage Percentage

Achieving 100 percent coverage may seem desirable as it implies a software application is completely free of bugs. It suggests that the test cases have encompassed all the criteria and requirements of the software.

However, evaluating the effectiveness of test scripts involves considering a broader range of possibilities. What if the test cases have inadvertently covered incorrect requirements? What if they have overlooked important needs? In such scenarios, relying solely on 100 percent coverage can lead to compromised software quality.

So, what is the ideal coverage percentage? The primary focus and goal for developers and testers should not be solely on attaining 100 percent coverage. Rather, it should be on creating comprehensive test scripts that are precise and well-defined. The analysis should incorporate scalable and robust test scripts that cover both the functional and non-functional aspects of the source code.

Type of Code Coverage Metrics

Coverage metrics for code serve as a means for developers to assess the thoroughness of their tests in exercising the codebase. These metrics offer valuable insights into testing effectiveness and can aid in identifying areas that require further attention. Let's delve into the four commonly used types of coverage metrics:

• Statement Coverage: This metric measures the percentage of code statements executed by the tests. A statement refers to a single line of code or an expression. Statement coverage indicates the extent to which the code has been executed at least once.

Formula: Statement coverage = (Number of statements executed by tests / Total number of statements) * 100

• Branch Coverage: Branch coverage focuses on decision points within the code. It calculates the percentage of decision points or branches traversed by the tests. A decision point arises when the code takes different paths based on a condition, such as an if-else statement. Branch coverage ensures that all possible execution paths are tested.

Formula: (Number of decision points executed by tests / Total number of decision points) * 100

• Function Coverage: Function coverage measures the percentage of functions in the code executed by the tests. Functions are blocks of reusable code that perform specific tasks. Analyzing function coverage allows developers to determine whether all defined functions have been invoked during testing

Formula: (Number of functions executed by tests / Total number of functions) * 100

• Line coverage: Line coverage measures the percentage of code lines executed by the tests. A line of code may contain multiple statements. Line coverage provides a broader overview of the code execution.

Formula: (Number of lines of code executed by tests / Total number of lines of code) * 100

These metrics assist developers in understanding the level of coverage achieved by their tests. However, it is important to note that high coverage does not necessarily imply comprehensive testing or the absence of bugs. Coverage metrics should be used in conjunction with other quality assurance practices to ensure thorough testing and reliable software delivery.

Code Coverage Tools

Code coverage tools have an important role in measuring and evaluating the coverage during Software Testing Life Cycle. This gives immense knowledge and information on the efficacy of the test suites, finding areas of improvement in software applications and helping ensure their quality. Here are some popular tools:

• JaCoCo: It is a code coverage tool for Java developers. You can generate code coverage reports using JaCoCo-Maven plugin to understand how much of your code is being tested. This open-source tool works seamlessly with popular integrated development environments like Eclipse IDE, making it convenient to use.
• Cobertura: It is an open-source tool specifically designed for measuring test coverage. It evaluates the execution status of lines of code within the test suite by instrumenting the code base. This allows Cobertura to determine which lines of code have been executed during testing and which ones have not.
• Clover: It improves test running efficiency by only executing tests that cover application code modified since the previous build.
• EMMA: It supports coverage analysis at the class, method, line, and base block levels. It provides aggregated metrics for source files, classes, and methods.
• Bullseye for C++: It is a tool for C++ and C languages. By instrumenting the C++ code, Bullseye gathers data on function coverage, branch coverage, and statement coverage. It generates detailed reports and visual representations to help developers assess the effectiveness of their test cases and identify areas that require further testing.
• RKTracer: RKTracer by RKVALIDATE is a tool designed for developers and testers, providing automated unit test generation and code coverage capabilities. It enables users to easily generate unit tests using intuitive user interfaces and validate code coverage metrics. This tool is handy for ensuring the safety of critical software in both native host and embedded target systems.

The tools mentioned above are just a few examples of popular coverage tools available for different programming languages. It's crucial that you find and select a tool that aligns with your software project's requirements, integrates well with your development environment, and provides the required coverage metrics for effective testing and analysis. These tools can also be integrated with cloud-based testing platforms like LambdaTest, enabling you to generate coverage for your websites and mobile applications across different browsers, devices, and platforms.

LambdaTest is a digital experience testing platform that enables you to execute automation tests while seamlessly incorporating code coverage tools such as JaCoCo, RKTracer, Serenity, and more. By leveraging these tools in conjunction with LambdaTest, you can generate comprehensive test reports on code coverage and ensure thorough testing of your application.

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Setting up Code Coverage

In Software Development Life Cycle, making complete use of code coverage requires the setup of essential configurations and tools. In the below steps, we will guide you on how to set up coverage in the production environment.

• Integration with Build Systems

One of the most crucial steps is integrating code coverage into your build system. The support of coverage tools allows the above integration in the build process. For example, if you use Maven, Gradle, or any other tools, it provides plugins and extensions which facilitate coverage integration.

However, the steps for integration depend on the build system. Despite this, the general process involves configuring the build scripts to allow coverage instrumentation and specifying the output format for coverage reports. Additionally, you need to have proper consultation of the documents and resources given by the build system.

• Configuring Coverage Metrics

In this step, you need to configure the coverage metrics before initiating the test and generating the coverage report. Depending on the type of code coverage chosen, different coverage metrics, like branch coverage, line coverage, etc., can be used to track the quality of the application. For example, if you want to gain insights into the execution of different branches within conditional statements, you might opt for branch coverage.

When you configure the coverage metrics, you specify the level of coverage details you need to address. With this, you can customize the analysis to align it with particular testing needs. It will help you focus on the aspects of coverage that are most crucial for your software projects.

• Enabling Coverage in Different Programming Languages

The coverage tools support different programming languages like C#, Python, Java, and others. This diversifies the use of the tools to have a complete coverage analysis. However, based on the programming language used, the setup process may vary. Still, the common steps remain the same, including installing and configuring the necessary coverage tools required to work with the development environment.

For instance, if you use Java, you might integrate JaCoCo, Emma, or Cobertura as your coverage tool. Similarly, Python developers can leverage tools like coverage.py or pytest-cov for coverage analysis. It's important to follow the documentation and guidelines provided by the specific coverage tool for your programming language to ensure accurate and reliable coverage measurements.

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Analyzing Code Coverage Reports

When the test process is executed, and a coverage report is generated, the next step is interpreting the obtained coverage metrics. This report gives valuable insights into the efficacy of the test efforts and shows areas of the software application’s codebase that need more attention. In the below section, we will discuss how to interpret coverage reports.

• Understanding coverage metrics: The coverage reports present different coverage metrics, which include statement coverage, branch coverage, function coverage, line coverage, and condition coverage. It is crucial to understand the coverage metrics to interpret the outcome appropriately.
• Analyzing coverage reports: When analyzing coverage reports, it is important to focus on areas with low coverage percentages or uncovered code of the software application. These areas of your codebase have not been adequately tested and may have possible bugs or vulnerabilities. You should emphasize the specific code segments like error handling mechanisms and complex algorithms which need comprehensive testing.
• Identifying uncovered code: In the coverage report, you get details on which branches, functions, and lines of code have not been executed during the testing. With this information, you can identify the specific areas of the code which need additional test cases. Based on the critical and high-risk areas, it is crucial to prioritize your efforts. Create targeted test cases for the uncovered part of your code. This aims to increase coverage and ensure complete and reliable testing.
• Dealing with false positives and negatives: As coverage reports give detailed information as mentioned above, it is important to be aware of the false positives and negatives. When the code is marked as “covered,” even though it has not been thoroughly tested or executed in all the possible scenarios, it results in a false positive. On the other hand, when the code is marked as “uncovered,” even though it has been tested or executed through different parts of the code.

Test cases need to be reviewed to address false positives and ensure the code is executed correctly. To mitigate false negatives, analyze the uncovered code segments, find if they are critical, and require more tests.

By analyzing coverage reports effectively, you gain insights into your testing efforts and can identify areas of your codebase that need improvements.

Code Coverage in Different Development Environments

Code coverage applies to various development environments, and understanding how to incorporate it effectively in different contexts is essential. In this section, we will explore coverage considerations for specific development environments.

Agile Development Development

Agile development methodologies, such as Scrum or Kanban, focus on iterative development, frequent releases, and collaborative efforts. Within this sphere, the following considerations prove invaluable:

• Early integration of code coverage: Integrate code coverage into the early stages of development, ensuring the consistent development and execution of tests. By leveraging continuous integration (CI) tools, tests can be automatically run, generating coverage reports with every code commit.
• The power of collaborative testing: Foster a culture of close collaboration among developers, testers, and team members to create comprehensive test cases. Encourage everyone to assume responsibility for attaining adequate coverage and identifying areas demanding improvement.
• Prioritizing user stories of paramount importance: Focus on writing test cases encompassing user stories with high business value and critical functionality. During sprint planning, accord these test cases priority, guaranteeing meticulous scrutiny of essential sections within the codebase.

CI/CD Pipelines

CI/CD pipelines automate the software development and deployment process, facilitating the seamless integration, testing, and deployment of code. Within this landscape, code coverage plays an important role in maintaining the software application's quality. Consider the following practices:

• Automated code coverage analysis: Seamlessly incorporate coverage analysis as an automated step within your CI/CD pipeline. This ensures consistent coverage measurement with every build, providing immediate feedback on coverage metrics.
• Gate coverage thresholds: Employ coverage thresholds as quality gates within your CI/CD pipeline. If coverage falls below the defined threshold, the pipeline can cease progression, barring the deployment of code that lacks sufficient coverage.
• Accelerating feedback loops: Strive for expeditious feedback on coverage results by integrating lightweight coverage tools and optimizing test execution time. This expedites the identification of coverage gaps, enabling timely remedial actions.

Test Driven Development (TDD)

Test Driven Development (TDD) represents an iterative approach that involves writing tests before writing actual code. Code coverage is important to ensure comprehensive test coverage within this paradigm. Consider the following practices for coverage in TDD:

• Red-green refactor cycle: This cycle involves writing the failing test (red), executing the less or minimum code for making the test pass (green), followed by refactoring the code. In the refactor step, the execution of coverage analysis is done, ensuring that the added test appropriately covers new codes.
• Granular unit test: Firstly, the unit test addressing and covering the individual code components of the software application is written and executed. With this, you get complete coverage analysis and facilitate isolating and addressing coverage gaps in specific code units.
• Use coverage as a metric: Utilizing coverage as a metric helps complete the test analysis in TDD. You should have a high test coverage percentage that ensures the codebase is appropriately tested and assures the software application's quality.
• Code coverage or Test coverages: Test Coverage and Code coverage are the most popular methodologies for measuring the effectiveness of the code. Though these terms are sometimes used interchangeably since their underlying principles are the same. But they are not as similar as you may think. Many times, I have noticed the testing team and development team being confused over the use of these two terminologies. Which is why I thought of coming up with an article to talk about code coverage vs test coverage in detail.

When you opt for coverage practice in different development environments, you can ensure that testing efforts align with each development environment's requirements.

Challenges with Code Coverage

Code coverage may not give complete information on the code executed and ensure the quality and reliability of the tests. Some of its common challenges are mentioned below:

• Even when a specific feature is not implemented in the design or code, the coverage report may still show 100% coverage.
• It alone cannot guarantee that all possible values or scenarios of a feature have been thoroughly tested.
• It will not give information on how much and how well you have covered your logic.
• If a certain function is not implemented as per the specification or included in the testing process, the coverage metrics will not identify this issue.
• It provides information on which lines or branches of code have been executed but does not assess the quality or comprehensiveness of the tests.

Best Practices for Improving Code Coverage

Improving coverage in your code is crucial to overcome its underlying disadvantages, as mentioned above. For this, it is important to have a strategic approach to testing and implementing best practices for improving coverage. Here are some of those:

• Setting realistic coverage targets: Before initiating to improve coverage for your code, you need to set realistic coverage targets that adhere to the software project requirement and its limitations. However, targeting 100% coverage may not be practical and challenging, especially in complex systems. Hence, having a coverage target is crucial to have higher coverage.

Those targets include considering the significance of different code sections, related risks, and resources available for testing. You need to prioritize areas needing higher coverage, like critical business logic, security-sensitive components, and error-prone sections. This allows for a reasonable coverage level in less critical areas.

• Writing testable code: Writing code that is easy to test is fundamental for achieving high coverage. Testable code is designed to make it easier to create meaningful and comprehensive test cases. Some best practices for writing testable code include:
• Break the code into modular components for easy testing of each component individually.
• Prefer small functions (self-contained block of code that performs a specific task or operation) and methods as they are easy to understand, test and maintain.
• Implement SOLID principles like Single Responsibility and Dependency Inversions that give codes that are easy to be tested.
• Incorporate dependency injection, as this will substitute real dependencies with mocks and stubs objects during testing.
• Prioritizing test cases for coverage: Not all test cases give equal coverage. It is crucial to prioritize test cases on the grounds of their potential impacts on coverage and ability to uncover bugs. Some common ways are:
• Focus on test cases with critical functionalities, edge cases, and error conditions.
• Create test cases that address boundary values that highlight areas where bugs are likely to occur.
• The created test cases should target complex code segments like nested loops, intricate algorithms, and conditional statements. Such areas have high coverage gaps and thus require thorough testing.
• Using mocks and stubs: For testing complex systems and dependencies, mocks and stubs are crucial tools. This helps in isolating components during tests, mimicking behavior, and managing test scenarios. With this, you can focus on particular code paths and test diverse scenarios that may be challenging to replicate in real-world conditions.

Integrating mocks and stubs into your test suite enables you to thoroughly test the interactions between different components, even if some dependencies are not fully implemented or available during testing. This promotes better coverage by providing controlled environments for testing various scenarios.

• Iterative Improvement and Maintenance

You need to improve code coverage and its maintenance continuously. This is because the codebase continues expanding, adding new features to the software application. At the same time, it is also important to review and evaluate coverage reports to find areas that need improvement. This helps you address coverage gaps and adds new test cases.

Code Coverage vs. Test Coverage

Code vs test coverage are two related but distinct concepts in software testing. Understanding the difference between them is crucial for software architects and testers.