What is Mutation Testing(Code Mutation Analysis)?

Mutation testing is a type of white box testing technique used to evaluate the effectiveness of a test suite by introducing small changes (mutations) to the codebase. These changes will simulate potential faults or bugs to check if your existing tests can detect them.

However, these changes don’t significantly affect the primary functionality of the software application.

This guide explains in detail about mutation testing, its types, when to use it, testing tools, techniques, metrics, and more.

Purpose of Mutation Testing (Code Mutation Analysis)

Here are the reasons why you must use Mutation Testing:

• The main purpose of mutation testing is to improve our testing quality. It will help the testers to identify the weak or missing tests in your test suit.
• Mutation testing can help you identify undetectable bugs through other testing methods.
• Mutation testing is an excellent method to achieve extensive coverage of the source program.
• Making small code changes and testing their impact helps find untested areas, improve test effectiveness, and strengthen code quality.

Features of Mutation Testing (Code Mutation Analysis)

Here are the key features of mutation testing:

• Test Quality Evaluation: It helps to identify the weak or missing test cases. It covers the edge cases that may not be covered under traditional testing. Ensures that testing doesn’t cover the code but also the expected validations.
• Mutant Creation: You need to introduce small changes in the code, which can be arithmetic, logical, or any kind of coding change.
• Mutation Score Detection: One can evaluate this testing with a simple below formula, Mutation score = (Mutants killed / total mutants) * 100
  Here, ‘mutants killed’ represents the number of defects detected by the test case, and total mutants represent the number of changes made inside the code.
• The Early Stage of Testing: Mutation testing is most effective when applied during the early stages of the software development of software applications. Detecting significant flaws in quality assurance approaches at this stage provides enough time to adjust test cases for effectiveness.
• Teamwork: Mutation analysis often requires teamwork and communication for success. Developing a collaborative environment helps prevent isolated pockets of information and potential misunderstandings. This collaborative approach ensures that every tester remains focused on their designated tasks.

Hypothesis around Mutation Testing

Mutation testing can improve the developer’s awareness of code quality by encouraging more test cases. It is based on several key hypotheses that justify its effectiveness in evaluating test suites.

• Competent Programmer Hypothesis: This theory posits that programmers typically produce code that is largely accurate, suggesting that most practical bugs are minor and straightforward errors instead of significant, intricate problems. Mutation testing, by introducing minor modifications (mutants), simulates realistic bugs that developers could potentially create.
• Coupling Effect Hypothesis: States that test cases that detect simple faults (small mutations) are also likely to detect more complex faults (multiple small errors combined). This suggests that if a test suite is effective at killing single mutants, it will likely catch real-world defects, which often result from multiple small mistakes.
• Redundancy Hypothesis: Implies that some mutants are redundant because they do not introduce meaningful changes or behave identically to others. Advanced mutation testing tools often filter out redundant mutants to optimize performance.
• Selective Mutation Hypothesis: Suggests that not all possible mutations need to be tested to evaluate test quality effectively. Instead, testing with a small but representative subset of mutants can still provide a reliable measure of test effectiveness while reducing computational cost.

Types of Mutation Testing

Mutation testing can be divided into three major types. They are:

1. Value Mutation

As the name implies, you should try to alter the values that we pass in the script. These changes can make something minor and significantly less critical in the software program. Value mutation is a way to modify the predefined values in the code to test how the program behaves under different conditions and to identify potential weaknesses or improvements.

For example,

//Original code:

let maxLimit = 10

if(maxLimit >= 10)

{

console.log(“You are within your limit”)

}

else

{

console.log(“More or less than the limit”)

}




//Modified code:

let maxLimit = 10

let mutantLimit = 5 //value is changed from 10 to 5

if(mutantLimit >= 10)

{

console.log(“You are within your limit”)

}

else

{

console.log(“More or less than the limit”)

}

In the above actual and modified code, the output will be different since we have modified the variable value.

2. Statement Mutation

Statement mutation testing is a type of mutation testing where individual statements in the source code are modified, removed, or replaced to check whether the test suite can detect these changes. The goal is to assess the effectiveness of test cases in identifying faults that affect program flow or logic execution.

For example,

//Original code

function calculateTotal(price, tax) {

let total = price + tax;

return total;

}




//Modified code

function calculateTotal(price, tax)

 { 

return total; // `total` was never assigned and removed the operation that was performed

}

3. Decision Mutation Testing

Decision mutation testing is a type of mutation testing that modifies decision-making constructs (such as if statements, loops, and conditional expressions) to evaluate whether test cases can detect logical errors in program flow.

//Original code

if (isAvailable) {

processOrder();

}




//Modified code

if (!isAvailable) { //Added not operator in the beginning of the variable

processOrder();

}

Since the condition is changed to not now, the if condition gets executed only if the isAvailable is false.

When to conduct Mutation Testing?

The aim of mutation testing is to validate the test cases prepared by the software test engineers. So, it is advisable to perform mutation testing before the unit test or when the software is in the early development stage.

By doing this, you can ensure that the test suite that is prepared can catch the bug in the early development stage. Developers can perform this testing before it is handed over to the QA team. Hence, it is performed during the unit testing phase that checks even for the most minor components of the software applications.

When not to conduct Mutation Testing?

Here is a list of scenarios when not to conduct mutation testing:

• Mutation testing needs more time and resource power to proceed. E2E tests are slow and require external dependencies (e.g., databases, APIs), making Mutation Testing inefficient.
• Code generated by frameworks, libraries, or tools (e.g., OpenAPI, ORM models) typically shouldn’t be mutated, as they follow standard structures. Mutation Testing should focus on custom business logic.
• If your objective is limited to black box testing for your software, focusing primarily on the front end or covering the entire testing phase, you may omit mutation testing.
• Mutation Testing can be computationally expensive, generating thousands of mutants. Running it on an unoptimized large codebase may lead to long execution times.

Advantages of Mutation Testing

Here are the advantages of mutation testing:

• The first and foremost advantage of mutation testing is to improve the test case coverage. Helps identify weak or ineffective test cases. Ensures that test cases effectively detect code changes.
• Since mutation testing is performed during early development stages, it can help developers to improve their code and identify potential issues that could occur after moving it to production.
• Mutation testing ensures that the edge case scenarios are covered that other traditional testing might not cover.

Disadvantages of Mutation Testing

Here are the disadvantages of mutation testing:

• The main disadvantage of mutation testing is that it is a more time and cost-consuming process as it involves huge resource allocation. This can slow down the development cycle if not optimized.
• Automation tools are effective for mutation testing. However, learning the tools and making the setup ready is another time-consuming process.
• Mutation testing is suitable only for doing white box testing, for an organization focused on black box testing, it is not necessary.

What elements are tested in Mutation Testing?

Mutation analysis assesses various aspects of software, such as code logic, variable values, statement execution, and error handling. It explores how the software reacts to changes in different areas. Below, we will discuss a few key aspects of code mutation analysis.

• Operators & control statements: Mutation Testing modifies mathematical and logical expressions to check if tests detect the changes. Modifications in if-else conditions, loops (for, while), and switch statements to check if the logic is properly tested.
• Variables & statement modification: Changes in variables, constants, or method parameters to test if values affect program execution. Removing, duplicating, or reordering statements to test if test cases detect missing or redundant logic.
• Test coverage: Code mutation analysis closely investigates existing testing procedures to ensure the identification of even minor issues that may impact user perception of the software applications. These tests may also assess the skills and competence of testers, as careful attention to detail is very important. You must pay attention to such information during testing to avoid missing critical mutations within the program.
• Exception handling: Changing how exceptions are handled to test if error conditions are covered.

Mutation Testing Metrics

Mutation Testing provides key metrics that help assess the effectiveness of your test suite and the robustness of your code. These metrics allow you to quantify how well your tests are catching faults (mutants). The most commonly used metrics in mutation testing include:

Mutation Testing Score

The Mutation Score is the primary metric in Mutation Testing, representing the effectiveness of the test suite in detecting mutants.

Mutation score = (killed Mutants / Surviving Mutants) * 100

• Killed Mutants: Mutants that cause the tests to fail, indicating that the test cases detected the mutation.
• Surviving Mutants: Mutants that do not cause the tests to fail, indicating that the test suite failed to detect the mutation.

Mutation Coverage

Mutation Coverage measures how much of the codebase is covered by the mutations. It helps determine which parts of the code were tested by mutations and which were not.

Mutation coverage = Number of code lines covered by mutants / Total number of lines * 100

• High Coverage: Ensures that your tests cover most of the code, including potential edge cases.
• Low Coverage: Indicates potential gaps in the code coverage, meaning that some parts of the application were not properly tested by mutants.

Mutation Survival Rate

The Mutant Survival Rate indicates how many mutants survived the testing process and were not detected by the test suite. A high survival rate often points to ineffective test cases.

Mutants survival rate = (Number of surviving mutants / Total number of mutants) * 100

• High Survival Rate: Indicates that many mutants (potential defects) were not detected by the tests.
• Low Survival Rate: Suggests that the test suite is catching most of the mutants, indicating strong test coverage.

Mutation Density

Mutation Density refers to the number of mutants generated per line of code, function, or class. This metric helps evaluate how many modifications (mutations) are being applied to the codebase.

Mutation density = Number of mutants / Number of lines of code

• High Mutation Density: Suggests that a large number of mutants are being generated for a small amount of code, which can be useful for deep testing but might also lead to longer test times.
• Low Mutation Density: Indicates that the mutations are sparse and that fewer tests are being applied to the codebase.

Phases of Mutation Testing

Mutation Testing is a multi-step process that involves several phases to ensure effective assessment of your test suite. Here’s an overview of the typical phases involved in Mutation Testing:

1. Understanding the requirement and writing test case: The requirement document is given and the person who performs mutation testing needs to understand it and start writing the test cases.

Testers create specific tests for the software application, focusing on implementing mutations that offer valuable insights. This phase sets up the comprehensive mutation analysis strategy and effectively describes the methods for introducing code mutations.

2. Making the mutation cases ready: The next step is to make the mutation test cases. Code mutation analysis involves its test documentation, including details about the mutated code and instructions for testers to rectify any issues.

Maintaining detailed records ensures the tests proceed as intended and helps the team bond to careful testing standards.

3. Environment setup: Since the cases to be executed are ready, now the next step of configuring the environment comes into the picture. As part of this, mutation testers establish a dedicated test server to serve as the platform for implementing mutations.

4. Mutation test case execution: The team can now perform the testing. The team has to modify the necessary code with the help of automation tools and start performing the mutation testing.

The mutation and software application testers must extensively document this process to ensure complete record-keeping.

5. Iteration: Based on the insights from the report, iterative improvements are made to both the test suite and the codebase.

The goal is to achieve a higher mutation score and ensure that the test suite is capable of detecting real-world faults in the code.

6. Optimization: In this phase, mutation testing may be optimized to address issues related to performance and equivalent mutants.

By focusing on important parts of the code or using techniques like mutant sampling, the testing process becomes more efficient.

Mutation Testing vs. Regression Testing

Here are the key differences between mutation testing and regression testing:

Top 5 Mutation Testing Tools

Here are some of the top 5 tools for Mutation Testing:

1. Stryker

Stryker is a tool used for mutation testing, designed to assess the quality of unit tests in your codebase. The core idea behind mutation testing is to introduce small changes (mutations) into your code and then run your tests to see if they catch the changes. If your tests fail when a mutation is applied, it’s an indication that the test is catching potential issues, thus proving its effectiveness. If the tests pass despite the mutation, then it suggests the test might not be covering that scenario properly.

Here’s how Stryker works:

1. Mutation: It modifies the code by applying small changes to it, such as changing a mathematical operator, negating a boolean, or swapping comparisons.
2. Test Execution: It runs your tests against the mutated code, checking whether your existing unit tests are sensitive enough to catch these changes.
3. Mutation Score: After testing, Stryker calculates the “mutation score,” which is the percentage of mutations that were killed by your tests (i.e., the number of times your tests failed due to a mutation). A higher score indicates better test coverage and test quality.

Stryker supports multiple languages and frameworks, including JavaScript (for frameworks like Mocha, Jasmine, and Jest), Java (JUnit), and .NET (NUnit), among others. It is highly customizable, allowing you to adjust the mutation testing process based on your project needs.

By using Stryker, teams can identify areas where their tests might be weak and improve the overall reliability of their codebase. It’s a powerful tool, especially in test-driven development (TDD) or when you’re looking to ensure your testing practices are solid and effective.

2. PIT Testing

The PIT Mutation Testing Tool (often simply referred to as PIT) is a mutation testing framework for Java that helps developers assess the quality and effectiveness of their unit tests. By introducing small, controlled modifications to the code, PIT ensures that the unit tests are thorough and can catch potential bugs.

Features of PIT Mutation Testing Tool

• Automatic Mutation Generation: PIT automatically generates mutants by making small syntactical changes to the source code.
• Test Execution: Once PIT generates mutants, it runs your unit tests to see if they can “kill” the mutants.
• Mutation Score: PIT calculates a mutation score, which is a percentage that indicates the effectiveness of your tests
• Informative Reports: Presents quickly readable reports with coverage data.

3. Jumble

The Jumble Tool is a mutation testing tool designed for Java. Like PIT, Jumble introduces small changes (mutations) into Java code to test the effectiveness of unit tests. The goal of using Jumble (or any mutation testing tool) is to assess whether your tests are robust enough to catch bugs that could arise from these small code modifications.

Key Features of Jumble

• Fine-Grained Control Over Mutations: Jumble allows fine control over what mutations are introduced
• Integration with Build Tools: Like PIT, Jumble integrates with build tools like Maven and Ant, making it easy to run mutation tests as part of your build process.
• Test Suite Evaluation: It evaluates the quality of your entire test suite by running all the tests against the mutants. This helps in detecting areas where additional or improved tests are needed.
• Actionable Feedback: The reports Jumble generates are easy to understand and actionable. They help you focus on improving specific tests that didn’t catch the mutants.

4. Jester

Jester is another mutation testing tool, but it is a bit different from PIT and Jumble in that it’s specifically designed for Java. Jester’s primary purpose is to perform mutation testing on Java programs, and it aims to improve the effectiveness of unit tests by making small, deliberate changes (mutations) to the codebase and testing if those changes are detected by the unit tests.

Benefits of Using Jester:

• Improved Test Quality: Mutation testing helps you assess whether your tests are really verifying the correctness of your code or if they are simply passing by coincidence. Jester helps you make sure your tests are meaningful and actually check for potential issues.
• Identifying Gaps in Test Coverage: Jester provides valuable insights into parts of your code that your tests might not cover, or where your tests are not sufficiently thorough.
• Confidence in Tests: By ensuring that your tests are robust enough to catch various mutations, Jester can give you greater confidence in the reliability of your test suite.
• Continuous Improvement: As you continue to run mutation tests with Jester, you can continuously track your mutation score, ensuring that your tests evolve and improve over time.

5. MutPy

MutPy is a mutation testing tool specifically designed for Python codebases. Mutation testing helps evaluate the effectiveness of unit tests by introducing small changes, or “mutations,” into the code. MutPy allows Python developers to assess and improve the quality of their tests by simulating potential bugs and ensuring their tests are capable of detecting them.

Key Features of MutPy:

• Mutation Generation: MutPy introduces small mutations into Python code, changing operators, values, or expressions.
• Test Execution: Once mutations are generated, MutPy runs the unit tests against the mutated code.
• Mutation Score: MutPy calculates a mutation score that reflects how effective the tests are in detecting the changes (mutations) introduced to the code.
• Detailed Reports: MutPy generates detailed reports that help developers understand how well their tests are performing.
• Customizable Mutation Operators: MutPy allows developers to define their own custom mutation operators if they want more control over how the mutations are introduced to the code. This flexibility can help ensure that the mutations align with the specific needs of your codebase.

What are the Techniques for changing the Mutant Program?

Here are the common mutation techniques used to modify the code and create mutants. Each technique introduces specific types of faults to test whether the unit tests can catch them.

• Arithmetic Operator Mutations: This technique involves changing arithmetic operators in the code.
• Relational (Comparison) Operator Mutations: These mutations involve altering the comparison operators used in the code (e.g., checking equality or inequality)
• Logical Operator Mutations: Logical operators such as &&, ||, and ! can be mutated to simulate potential bugs introduced by incorrect Boolean logic.
• Constant mutations: This technique involves changing constant values in the code. Constants can be changed to other values (numbers, booleans, etc.) to simulate possible logic errors introduced by incorrect constants.
• Method Call Mutations: This mutation changes method calls or their return values. It simulates cases where a method might be replaced with a different method or where it could return incorrect values.

How to perform Mutation testing?

Here are the steps to perform Mutation Testing:

• Step 1. Write Your Unit Tests: Mutation testing is only useful if you have a set of unit tests in place. These tests serve as the baseline for detecting the changes (mutations) introduced into your code.
• Step 2. Install a Mutation Testing Tool: There are different mutation testing tools for different programming languages. Select one that is appropriate for the language and framework you are using
• Step 3. Configure and run Mutation Testing Tool: Once you’ve installed the tool, configure it to work with your codebase. This typically involves specifying which files or classes to mutate and where the tests are located. Then the tests are executed.
• Step 4. Analyze Mutation Results: After mutation testing is complete, the tool will produce a report with details about the results, typically in HTML or JSON format.
• Step 5. Repeat the Process: Each time you refactor or add new features to your code, it’s a good idea to run mutation testing to confirm that your tests still provide high-quality coverage.

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Interpreting Mutation Testing Outcomes

The goal of mutation testing is to measure how well your unit tests can catch small changes (mutants) introduced to your code. By analyzing the results, you can gain insights into the quality of your tests and identify areas for improvement.

• Mutation Score: The mutation score is the primary metric that summarizes how well your tests detect the mutants (introduced faults).
• Surviving Mutants: Surviving mutants are mutants that were not detected by your unit tests (i.e., your tests passed even after the mutations were introduced).
• Killed Mutants: Killed mutants are mutants that were detected and killed by your unit tests (i.e., your tests failed when the mutants were introduced).
• Mutation Testing Time: Mutation testing tools can be time-consuming as they create many mutants and execute tests against them. The time it takes to generate mutants and run the tests can be an important metric.

Common Errors in Mutation Testing

Mutation tests primarily bring to light issues within the testing procedure itself. With this in mind, here is a spectrum of problems that these evaluations can assist in identifying:

1. Low Mutation Score Due to Insufficient Test Coverage: A low mutation score often indicates that your tests aren’t covering enough of your code or important edge cases, leading to many surviving mutants.

These test cases might need more specificity or comprehensiveness to align with the team’s testing requirements. These documents should include every practical scenario the team may encounter during software testing to ensure reliability.

2. Mutation Testing Performance Issues: Mutation testing is computationally expensive and can take a long time, especially for large codebases with complex tests. This can make mutation testing impractical if it is part of a continuous integration pipeline.

3. Ignoring Test Dependencies or Not Running Tests Properly: Incorrect setup of the mutation testing tool or missing configuration files may prevent some tests from running. Similarly, some tests may be ignored or skipped, leading to incomplete results.

4. Unoptimized code: Mutation testing can show pre-existing issues within the software applications. For instance, testers attempting to mutate the code may detect the critical defects themselves. This is another valuable aspect of code mutation, as it exposes flaws beyond the testing process.

The more extensively testers test the code in any capacity, the more issues the team can uncover and rectify during the testing phase of the software application.

Common Mistakes when performing Mutation Testing

Mutation testing is a powerful technique to assess the effectiveness of a test suite, but many teams make mistakes that can lead to misleading results, wasted time, or unnecessary complexity.

Here are some of the most common mistakes when performing mutation testing and how to avoid them:

1. Confusing Code Coverage with Test Effectiveness: Code coverage only tells you whether lines of code were executed; it does not indicate if the tests can detect faults (mutants).

A high coverage percentage does not guarantee that the test suite is catching all potential bugs. Use mutation testing in addition to code coverage to verify that tests actually validate expected behavior, not just execute the code.

2. Running Mutation Testing on the Entire Codebase: Mutation testing is computationally expensive, and mutating every single function/class can take an extremely long time.

Focus on high-risk or complex parts of the code, such as critical business logic, frequently changed code or areas with past bugs.

3. Not Configuring the Mutation Tool Correctly: Configure the tool to exclude test files, third-party libraries, or auto-generated code. Focus on specific classes or functions that are critical to the application.

Use parallel execution if supported by the tool to speed up testing.

4. Ignoring Equivalent Mutants: Assuming that all surviving mutants indicate missing test coverage and failing to account for equivalent mutants (mutants that do not change program behavior).

Equivalent mutants make it seem like tests are weak, but in reality, they are mutations that cannot be detected. This leads to wasted effort in trying to “kill” them. Manually review surviving mutants to identify true faults vs. equivalent mutants.

Best Practices for Mutation Testing

Follow these best practices for Mutation Testing:

• Use Mutation Testing Alongside Code Coverage: Code coverage only tells you if a test executes a line of code, but mutation testing tells you if the test validates the correct behavior.
• Focus on Critical and Complex Code: Run mutation testing on the most critical and complex parts of your code first. Running mutation testing on the entire codebase is slow and unnecessary for trivial code.
• Handle Surviving Mutants Effectively: Investigate all surviving mutants to determine if they are a real issue or an equivalent mutant. Some mutants survive because the test suite is weak, while others survive because they are equivalent mutants (mutations that do not change program behavior).
• Configure Mutation Operators Wisely: Adjust mutation operators to match your needs. Some mutation operators generate too many trivial mutants, which can lead to unnecessary test improvements.
• Randomly select mutants: While prioritizing specific components of software applications for thorough testing, it is advantageous for testers to randomly choose mutants to include, especially when facing tight deadlines. As long as these selections include various significant mutation types, the quality assurance team can validate the overall effectiveness of their software testing strategy.

Why perform Mutation Testing on Real Devices?

Mutation testing helps identify weak test cases by making small code changes and checking if tests catch them. Running these tests on real devices ensures accurate results, detecting issues that emulators or simulators might miss.

BrowserStack is a cloud-based platform that provides access to real devices and browsers for testing. Integrating BrowserStack with your mutation testing process can help you test on real devices and browsers. It also ensures that your test suite is effective in identifying potential issues (mutants) across a variety of real-world environments. With BrowserStack you can:

• Run mutation tests on 3500+ real devices & browsers
• Catch hidden bugs in real-world conditions
• Ensure robust test coverage across all environments
• Use mutation testing tools like PIT (for Java), Stryker (for JavaScript), MutPy (for Python), or Muter (for Swift) in your CI/CD setup. BrowserStack Automate integrates with Jenkins, GitHub Actions, and other CI/CD tools to run mutation tests seamlessly.
• Run multiple test cases for mutation testing using BrowserStack’s parallel test execution on different environments.

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Conclusion

Mutation testing is a powerful technique for evaluating the effectiveness of a test suite by introducing small changes (mutants) in the code and checking whether the existing tests can detect them.

Unlike traditional code coverage metrics, mutation testing provides deeper insights into test quality by ensuring that tests not only execute the code but also validate its correctness. These mutations allow testing teams to assess their methodology and measure its efficiency in identifying and addressing errors within the source code.

This approach aligns well with automation processes, enabling organizations to validate the software applications they rely on for testing procedures. Hence, you should incorporate mutation testing in your software project to validate its efficiency and accuracy.