Mutation Testing: Its Concepts With Best Practices

Mutating testing is a software testing technique used to assess the quality and effectiveness of your test case by making deliberate changes or “mutations” into the code to simulate potential defects. In modern software development, the increasing complexity of software applications, coupled with the demand for rapid feature development, has raised the critical issue of ensuring the robustness and reliability of software code. Even minor undetected bugs or errors can have consequences, leading to financial losses, security breaches, and compromised user experiences.

Traditional software testing methods primarily focus on improving the quality of software applications, often with a limited ability to uncover every potential defect. This deficiency necessitates the development of more advanced testing approaches that ensure the quality of software applications and assess the testing process's effectiveness.

Mutation testing emerges as a solution to this problem. It is an active testing process designed to evaluate the integrity of software applications by intentionally introducing small, purposeful changes to the source code. The goal of these mutations is to create errors or bugs, which, in turn, enhances the overall software testing strategy.

This guide aims to provide in-depth information on mutation testing and its associated concepts by familiarizing you with when and how to use mutation testing with best practices.

Let us begin the discussion by understanding the term “mutation” in the context of software testing.

Understanding the concept of mutation testing

A mutation is a minor modification in the source code, typically achieved by altering a line of code within software applications. These modifications can take various forms, such as deleting or duplicating lines of code, modifying true or false expressions, or altering the values of variables. It's important to note that these changes are intentionally minimal and don't significantly affect the primary functionality of the software application. Subsequently, the code containing these mutations undergoes thorough testing compared to the original unaltered code.

Now that we know what mutation does let's understand what mutation testing is and how it can be used in software development, its essential purpose, and its characteristics.

What is mutation testing?

Mutation testing, called code mutation analysis, is a testing approach in which specific elements of a software application's source code are altered. Subsequently, tests are performed to determine whether these modifications lead to test failures.

In simpler terms, code mutation analysis is about making minor changes to your code and then running your unit tests against the modified code, expecting the tests to fail. If the tests don't fail after changing the code, you should improve how your tests are designed to catch potential issues.

In code mutation analysis, your production code gets inserted with minor "defects" or "mutants." Each of these mutants is then subjected to testing using your test suite. Notably, the changes made to these mutants are kept minimal to avoid significantly affecting the overall functionality of your software. If your tests detect a problem when a mutant is introduced, we say that mutant is "killed." On the other hand, if your tests don't catch any issues despite the mutant's presence, the mutant "survives." The effectiveness of your tests is determined by the percentage of mutants that get "killed."

The main objective of mutation testing is to assess the robustness of test cases when the source code is modified. This testing methodology is often called a Fault-based testing strategy, which intentionally introduces faults into the code. This type of mutation testing falls under white box testing and is typically used for unit testing.

Now that we understand that mutation testing involves adding small code snippets with minor changes to the original code and then testing this modified code as unit tests explore our comprehensive unit testing tutorial. To gain valuable insights into trying small units of code effectively.

Here are some essential characteristics and purposes of mutations in software testing:

Characteristics of code mutation analysis are as follows.

• They replicate errors within the codebase.
• Mutations are modified versions of the original code, often called "mutants."
• This process entails changing elements such as arithmetic and relational operators, adjusting constants, or modifying control structures.

The purposes of code mutation analysis are as follows.

• Detecting inadequately tested portions of code.
• Identifying concealed defects that might remain undetected by other testing methods.
• Uncovering new types of errors or bugs.
• Determining the mutation score indicates the effectiveness of your testing process.
• Investigating error propagation and state infection within the program.
• Assessing the quality of your test cases.

Hypothesis around mutation testing

Code mutation analysis can improve developer awareness and code quality by encouraging more robust test cases, and this code mutation analysis is based on two hypotheses.

• Competent Programmer Hypothesis (CPH)

This hypothesis assumes that the programmer is skilled, leading to code that approaches perfection. Any identified defects are expected to be minor syntactic errors that can be quickly resolved.

• Coupling Effect Hypothesis

This hypothesis relates to the defects examined in code mutation analysis. It proposes that when simple mutants containing minor errors combine or interact, they can create more complex and significant issues. A test suite designed to find simple problems in a system will also be quite effective at discovering many problematic errors that arise from these simple issues.

Types of mutation testing

There are different types of mutation testing. Here, we'll focus on three primary types of mutation testing.

Value Mutation

It involves changing the values of constants, method parameters, or loop variables in a software program to create a modified version of that software program. 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.

Original Code (Java):

 int originalValue = 10;
       if (originalValue > 5) {
       System. out.println("Original code: Value is greater than 5.");
       }

Mutant Code (Value Mutated):

 int originalValue = 10;
      int mutantValue = 2; // Changed from 10 to 2
      if (mutantValue > 5) {
      System.out.println("Mutant code: Value is greater than 5.");
      }

Decision Mutation

The logical and arithmetic operators used within a program are modified in decision mutation. These changes impact the application's decision-making processes and subsequently alter its results. For instance, consider an 'if' statement that initially executes when (a > b). This operator is changed to (a < b) in the mutant code, fundamentally changing the program's decision-making logic.

Original Code (Python):

 a = 10
 b = 5
 if a > b:
    print("Original code: a is greater than b.")

Mutant Code (Decision Mutated):

a = 10
b = 5
if a < b:  # Changed from a > b
print("Mutant code: a is less than b.")  # Changed message 

Statement Mutation

It involves changing complete code statements to generate a mutant version of the applications. This modification can include deleting an entire statement, re-ordering statements within the code, copying and pasting statements to different locations, or replicating certain statements from the original code.

Original Code (C++):

int x = 5;
int y = 10;
int result = x + y;

Mutant Code (Statement Mutated):

int x = 5;
int y = 10; // Mutated: Statement removed
int result = x - y; // Changed operation from addition to subtraction

Features of effective mutation testing

Practical mutation analysis is essential for identifying and improving software robustness. Here are a few key features that make mutation analysis effective:

• Test coverage

These tests cover every vital aspect and functionality of the software application. Organizations could generate a mutation test for every standard test case in situations where resources allow. The specific number of tests may differ depending on the organization's abilities and choices, but practical mutation tests examine different aspects of the code.

• Strategic planning

Practical mutation tests in software applications are carefully planned and strategically structured to align with the organization's overall testing objectives. These tests produce errors that resemble real-world failures, allowing testers to anticipate and resolve such issues naturally. This significantly enhances the organization's testing procedures.

• Constructive feedback

Mutation tests are designed to reveal defects in the testing processes, demonstrating how the team can improve their checks and rectify minor errors as they arise. It's crucial to prioritize 'invalid' mutants that affect the software's functionality because this approach enables more precise enhancements in testing throughout the project. In this way, mutation analysis offers valuable feedback to enhance testing procedures and deal with minor software development errors.

• Proactive approach

Practical mutation tests serve as a validation mechanism for the team's overall test strategy. Therefore, they are 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.

• Consistency

Consistency is essential in mutation tests, even across different iterations of a software application. Results from mutation tests remain reliable and consistent, even when accommodating software changes. Subsequent tests must uphold the same level of attention to detail to maintain their effectiveness, as precision is crucial to accurate mutation tests.

• Subtlety

Mutation tests aim to assess the quality assurance team's ability to identify code defects through their tests and third-party platforms. Consequently, these tests should only immediately stand out to some observers of the software. The goal is to evaluate how testers respond to minor code issues with delicacy.

• Collaboration

Like other software testing processes, code 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.

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Advantages of mutation testing

The benefits of code mutation analysis are as follows.

• It is a potent approach to achieve extensive source program coverage. Code mutation analysis can mimic the errors and expect that the test suite accurately detects them. It led the team to create complete test cases, which led to extensive source code coverage.
• Comprehensive testing of the mutant program becomes possible. The test suite is subjected to various scenarios, including edge cases and potential issues that traditional testing methodologies might not cover.
• Code mutation analysis enhances error detection in the code for software developers. For example, if the test case cannot detect a mutant in the software application, a gap in the test coverage shows an issue in the code. This feedback is shared with the developers to fix the identified problem at the early stage of the development of software applications.
• This method uncovers ambiguities within the source code and can detect all program faults. Since it creates mutants that represent various potential problems, it can see a wide range of program faults, including subtle logic errors, boundary conditions, and incorrect assumptions made in the code.

Disadvantages of mutation testing

Conversely, Code mutation analysis has some drawbacks:

• Code mutation is exceedingly costly and time-consuming due to generating numerous mutant programs. It is one of the primary limitations of code mutation analysis, as using mutation operators to create mutants can make the codebase complex, and fixing it becomes expensive and time-consuming.
• Automation tools are vital for efficient execution. As many mutants are involved in mutation testing, executing such a high number creates a dependency on automation testing tools. It requires a learning curve and setup efforts.
• Each mutation entails the same number of test cases as the original program, potentially necessitating extensive testing against the original test suite. It eventually led to an increase in testing time and resource requirements.
• This method is unsuitable for black box testing because it involves source code modifications. It's most suitable for white box testing, where the tester can access the code's internal details.

Why is mutation testing important?

Code mutation analysis serves as a critical evaluation tool for your testing procedures. At first glance, it may introduce additional complexity to your software application development, particularly if you already implement test-driven development.

Test coverage is a valuable metric that indicates how much of your code is tested. Yet, it only assures that your tests can catch possible errors or software bugs. Even with extensive test coverage, poorly designed tests may overlook security vulnerabilities. Code mutation analysis provides developers with numerous benefits. Code mutation analysis offers some significance, some listed below.

• Evaluating test suite effectiveness.

It allows you to analyze your test suite's effectiveness in identifying faults and vulnerabilities in the software applications.

• Identifying areas for test improvement

By pinpointing weaknesses in your testing strategy, mutation testing helps you identify areas where test enhancements are needed in your developed software applications.

• Detecting regression causes and timing

It assists in identifying when and why regressions occur within your test suite, helping resolve issues quicker.

Despite their significance, it is essential to understand and know where and when you must use mutation analysis. In the following sections, let us know when mutation analysis can be performed and when not to perform code mutation analysis in detail.

When to perform mutation testing?

We know that the main aim of mutation analysis is to validate and verify the quality assurance process for software applications. Before the Quality Assurance (QA) team performs the test process, it is advised to get credibility to ensure the software applications' reliability. Therefore, mutation analysis is needed early in the test process. This ensures that if the testing suite cannot identify and eliminate mutants, there is sufficient time for the QA team to implement significant improvements in the testing procedures.

Moreover, due to its adaptability and suitability for all types of software, including web, mobile, and desktop applications, it is crucial to add this type of testing in the early development stages. Hence, it is performed during the unit testing phase that checks even for the most minor components of the software applications.

When not to perform mutation testing?

There are scenarios in software applications where you might find it unnecessary to conduct mutation analysis, and there are valid reasons for this decision. For instance, 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.

Organizations sometimes consider white box testing excessively time-consuming and labor-intensive, leading them to forgo mutation analysis. Additionally, when the test cases have been carefully reviewed and validated by quality assurance (QA) professionals, mutation analysis might be skipped to conserve testing time and resources.

What aspects 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.

• Test cases

It is a complete document containing detailed information about each test, including expected outcomes. Well-structured and accurate test cases provide insights into the application's quality and alignment with the organization's performance expectations. The data within these test cases plays a crucial role in evaluating a tester's ability to detect specific defects, including those introduced by mutation testing.

• Testing standards

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.

• Individual code units

Mutation tests are commonly used during the unit testing phase of development. They focus on individual code components, optimizing the testing process by directing testers to work with specific lines of code. Since mutation tests often occur early in the quality assurance stage, they can enhance testing efficiency without compromising accuracy.

• Program updates

Software updates typically require restarting the testing process to ensure the absence of new faults and prevent previous errors from recurring. Repeating mutation tests is crucial in maintaining consistent testing standards following significant software changes. Thorough post-update checks, driven by code mutation, underscore the importance of testing at every stage of development.

• Automation software

Organizations use mutation analysis to evaluate the effectiveness of their automated test suites in identifying mutated code and other issues. The ability of third-party testing applications to detect external changes in a program and potentially rectify them builds confidence in the automation process. Validating the automation approach assures testing teams.

• Automation Strategy

Adding automation into a company's workflow is as essential as choosing automation software. Organizations can consider techniques such as hyper-automation, enabling smart mutation selection, and software tests for automation. A strong automation plan adapts to the different code types within an application and avoids conflicts that make specific tests incompatible with automation, extending the platform's potential.

• The Application

While code mutation analysis primarily serves the testing team rather than the application, it can still provide valuable insights. For example, it uncovers how the software handles alterations in its code and if it communicates these issues effectively in line with the team's expectations. Although not a direct software testing method, mutation testing provides exciting insights into the application's inner workings.

Manual testing can help you manage a few cases if the software application is minimal, but imagine having to build an ERP system with 300+ test cases. In this case, as the software becomes more complex and challenging to manage, there is always a need to improve the testing process by any means necessary.

Building and maintaining a digital asset for a modern-day business can be challenging. To help you navigate this process effectively, watch this video on how to get started with automation testing. It's a valuable resource that can give you essential insights and strategies for successful automation testing.

Subscribe to the LambdaTest YouTube channel for more videos on Automation testing and Cypress testing and to elevate your testing game!

Metrics in mutation testing

Assessing the effectiveness of your code mutation analysis is crucial, and it involves several metrics like mutation score, mutation adequacy, surviving mutants, mutation density, equivalent mutants, and mutation survivability. These metrics are pivotal in understanding your test suite's quality and testing strategy. Let us explore each of them in detail.

Metrics in mutation testing

Assessing the effectiveness of your code mutation analysis is crucial, and it involves several metrics like mutation score, mutation adequacy, surviving mutants, mutation density, equivalent mutants, and mutation survivability. These metrics are pivotal in understanding your test suite's quality and testing strategy. Let us explore each of them in detail.

• Mutation score

One effective way to evaluate the effectiveness of mutation analysis is by utilizing a mutation testing score. This score represents the ratio of mutants eliminated to the total number of mutants and offers a quantitative measure of your test suite's quality. The ideal mutation score is 100% or 1, indicating that the test suites have eliminated all mutants.

Mutation score formula:

Mutation Score = (Number of Killed Mutants / Total Number of Mutants)

Expressed as a percentage:

Mutation Score (%) = (Number of Killed Mutants / Total Number of Mutants) * 100%

A mutation score of 100% signifies that the test cases are mutation-adequate, showcasing the effectiveness of this approach in assessing test case quality. However, it's important to note the significant drawback: the high cost of generating mutants and running test cases against them.

• Mutation Adequacy

It represents the number of mutants killed in the software program while performing mutation analysis. You generally express mutation value in percentage and help to find how many mutants are detected and flagged as errors. When you get a high mutation value, you analyze whether the test suite is appropriate for finding the code defects.

Mutation adequacy formula:

Mutation Adequacy (%) = (Number of Killed Mutants / Total Number of Mutants) * 100

• Surviving mutants

The test suite does not detect specific mutants when you perform mutation testing. It means that those mutants are not killed and are considered to be survivors. When you calculate their number in percentage, it is denoted as surviving mutants. It allows for the identification of the weaknesses or limitations of the testing process.

Surviving mutants formula:

Number of Surviving Mutants = Total Number of Mutants - Number of Killed Mutants

• Mutation density

This metric in the mutation testing can assess how many changes or mutants are present in a specific unit of codes. You calculate the number of mutations introduced in a particular part of code compared to the size of that code. However, the unit of code mainly varies. This means that calculation is done as mutations per line of code, mutations per method, or mutations per file. With these metrics, you get information about the density of potential defects in the codebase.

Mutation density formula:

Mutation Density = Total Number of Mutants / Size of the Code Unit (e.g., lines of code, methods, files)

• Equivalent mutants

When a change in the code line of the program is done, it doesn't have to affect the behavior of the software application. Some changes do not impact their functionality and are functionally equivalent to the original code. Such changes are called equivalent mutants, which can be calculated as per the formula:

Equivalent mutants formula:

Number of Equivalent Mutants = Total Number of Mutants - Number of Non-Equivalent Mutants

• Mutation survivability

This metric of mutation analysis measures how the changes in the line of code consistently survive across different test and test environments. It gives information about the reliability and correctness of the testing strategy.

Mutation survivability formula:

Mutant Survivability (%) = (Number of Test Runs Where Mutant Survived / Total Number of Test Runs) * 100

Understanding these metrics in mutation analysis is vital to assess software effectiveness. Explore our comprehensive tutorial on software testing metrics and QA metrics to optimize your testing and enhance software quality.

What are the phases in mutation testing?

Code mutation analysis involves several distinct phases that collectively evaluate the robustness of software testing. These phases include mutant generation, test suite execution, and mutation score calculation. Below, we will get into each step to better understand how code mutation analysis works.

• Requirement assessment

In the initial stage of the mutation analysis lifecycle, the emphasis is on pinpointing the areas requiring validation and deciding which part of the application's code would gain the most from these tests. It might involve discussions with developers and executives to address their concerns efficiently.

• Test planning

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.

• Test case creation

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.

• Test environment configuration

Testers ensure that the software applications are prepared for code modifications and have protocols to address any issues that may arise if other team members fail to detect them. As part of this, mutation testers establish a dedicated test server to serve as the platform for implementing mutations.

• Test execution

Once all preparations are complete, testers modify the code in various components of the software applications and then await detection and resolution of the issues by other testers. The mutation and software application testers must extensively document this process to ensure complete record-keeping.


• Test cycle conclusion

Upon concluding the testing phase, mutation testers verify that all the modifications they introduced have been addressed by the application testers or themselves. The test cycle is then closed, and the results are analyzed, with discussions on how testers responded to different errors and their effectiveness in rectifying them.

• Test iteration

Following the test cycle's closure, it may be necessary to reactivate it when future software updates are introduced. Every alteration to the application impacts its functionality to some extent, introducing new potential issues that the team must consider to maintain a thorough testing process.

Additionally, regression testing plays a pivotal role in the mutation analysis lifecycle as it ensures that the software continues to function correctly after introducing code modifications and as new software updates are introduced. It helps maintain the integrity of the software and ensures that previously identified issues do not reoccur.

In the following sections, we will see the difference between regression testing and code mutation analysis.

Mutation Testing vs. Regression Testing

Regression Testing aims to catch regressions, while code mutation analysis targets specific code mutations. Knowing the scope of each method helps in effectively identifying and addressing different types of issues.

Tools for mutation testing

Code mutation analysis is essential for assessing the robustness of your code and the effectiveness of your test suites. These tools play a vital role in identifying potential weaknesses in your software.

Below, we will learn some mutation analysis tools to understand their features better.

Stryker:

Stryker provides intelligible reports that help identify surviving mutants, improving test suite effectiveness.

Stryker’s features:

• Mutation Variety: Supports over 30 mutation types.
• Efficiency: Utilizes code analysis and parallel test runners for speed.
• Test Runner Compatibility: Works seamlessly with various test runners.
• Open Source: Maintained by the open-source community on GitHub.
• Multilingual Support: Compatible with JavaScript, TypeScript, C#, and Scala.

PIT:

PIT Mutation Testing (PITest) is an open-source Java-based tool used in software testing to assess the quality of test suites by introducing artificial defects (mutations) into the code and checking if the tests can detect these mutations.

PIT’s features:

• Speed: Analyze code in minutes.
• Ease of Use: Integrates with popular build and test tools.
• Ease of Use:Active Development: Continuously developed and supported.
• Informative Reports: Presents quickly readable reports with coverage data.

Jumble:

Jumble is a mutation testing tool for Java, operating at the bytecode level.

Jumble’s features:

• Unit Testing: Run unit tests and categorize mutations.
• Mutation Outcomes: Identifies "Passed Mutations" and "Failed Mutations."

MutPy:

MutPy is a mutation testing tool for Python applications.

MutPy’s features:

• Comprehensive Mutation Tests: Supports high-order mutations.
• User-Friendly Interface: Easy-to-use output analysis.
• Customization Options: Tailorable to specific testing requirements.
• Abstract Syntax Trees: Enhances tester confidence with structured source code views.

You can choose any code mutation analysis tools that best align with your programming language and development. For example, you can select Stryker for JavaScript. When done, you can develop a complete test suite for your software application. However, you must ensure that your created test suite covers all the relevant portions of the codebase.

You can also test your software application on a cloud-based platform like LambdaTest, as it allows you to verify the working and performance of the software application across various browsers and devices.

LambdaTest is an AI-powered test orchestration and execution platform that enables you to run manual and automation testing across a wide range of more than 3000 devices, browsers, versions, and OS combinations. Its primary focus on functional and cross-browser compatibility testing works effectively with mutation testing, which analyzes code for defects and weaknesses. By integrating these two methodologies into your testing process, you can ensure the delivery of high-quality, robust, and bug-free web applications to your users.

Stakeholders Involved in Mutation Testing

In code mutation analysis, different stakeholders are involved in mutation testing, each with a specific role. Some of them are as follows:

• Mutation testers

Intentionally introduce minor defects to confirm proper testing procedures. They are typically part of the quality assurance team.

• Application testers

Regularly inspect the code, identify and fix mutations, and perform white-box testing and other techniques.

• Application developers

Create program features, write initial code, and address issues testers identify for software stability.

• Project managers

Guide application development and maintain high standards at all stages.

The roles mentioned above in code mutation analysis involve incorporating mutants in the software application and performing mutation tests. Let us learn this in detail from the below-given section.

Techniques to change mutant program

When it comes to mutation testing, changing the mutant program is crucial. It involves using different techniques to make code alterations for testing. Let's delve into these techniques for modifying the mutant program.

• Operand replacement operators:

In this technique, you are changing the operands in the application’s code, which include variables and constants used in the expressions of the application. To put it in simpler terms, let's take an example: Imagine your original condition is If ( x > y ). You will replace x with y. So it becomes If ( y > y ). Another way is to replace y with any number, like If ( 5 > y ). You are doing this to check how your software application responds to different input values and expressions.

• Expression modification operators:

In this technique, you are focused on changing the operators or inserting any new operators in the statement/code line of the software applications. For example, if the original condition is If ( x==y ). You will mutate this by replacing ‘==’ with ‘>=.’ It will lead to a change in the code as If ( x>=y ). Otherwise, insert an increment operator ‘++’ to have If ( x==++y ). It is being done to assess the functionality of the software application being handled and managed in logic or arithmetic operations.

• Statement modification operators:

In this technique, statement modification operator changes are done to the programmatic statements or code. Here, you have two options. First, you can delete part of statements, and second, you can delete complete statements to observe the functionality of the software applications.

Let's take an example. You can delete the else part in an if-else statement to see how your software application behaves when the else condition is absent. Alternatively, delete the entire if-else statement to test how the software application responds without that condition. This technique effectively identifies how software applications handle different control flow scenarios, a sequence in which statements and instructions are executed within a software application.

Along with some traditional techniques, code mutation analysis offers some bonus techniques.

• GOTO label replacement

Here, you simply change the GOTO statement.

• Return statement replacement

This technique involves modifying the value and conditions within a function in a software program, which are generally blocks of code returned in a function. Imagine you have a simple function in your program. This function calculates the square of a number in Python:

def calculate_square(x):
       return x * x.

In this case, the function takes a number 'x' as input and returns its square. Now, change the condition inside this function for mutation testing. You can modify it like this:

def calculate_square(x):
   return x + x  # Changed from x * x to x + x.

You have altered the condition from multiplication (x * x) to addition (x + x). This change is part of the mutation testing process to see how the function behaves with this modification.

• Statement deletion

In the source code, any specific line is deleted from the application and evaluated for functionality. Here is an example:

# Original Code
   def add_numbers(a, b):
       result = a + b
       return result


# Mutated Code (Deletion of 'result' assignment)
   def add_numbers(a, b):
   return a + b

• Unary operator insertion

There are different unary operators in the code for any functionality of the software application, like negation (“--”) or increment or decrement (“++”). You unary operators to the code line of the program and check how the software application functions.

# Original Code
   def negate_number(x):
   return -x


# Mutated Code (Insertion of the unary operator '++')
   def increment_number(x):
   return ++x

• Logical connector replacement

This involves changing the logical connector like &&" (AND) to "||" (OR) or vice versa in conditional statements. With this technique, you can test if the program can make the correct decision when conditions are changed.

• Comparable array name replacement

In this technique, you replace one array, a data structure that allows you to store multiple values of the same data type under a single variable name with a different one so that you can compare in the program. With this technique, you check how software applications can accurately compare and operate on an array in its replacement or renaming. Hence, you can find array-related bugs easily with this method.

• Removing the else part in the if-else statement

In this technique, you remove the else part in the code line from an if-else statement. You can check your software application’s functionality when it is removed from the code.

• Adding or replacing operators

In this, you change the arithmetic and logical operators in the expression of the code. For example, you replace “+” with “*” or “&” with “I”.

• Statement replacement by changing the data

In this technique, you replace the data value or the expression in a code line with different values or expressions. The data value can be any constant used in a program. Further, expressions are a combination of data values, operators, and variables. When you replace these in code, you can evaluate how the software application responds to changes in input data.

• Data modification for the variables

You can change the code values linked with variables. It tests ways the program can handle different data scenarios, revealing potential bugs related to variable handling.

• Modification of data types in the program

In this technique, you change the data type from an integer to a string in the program's code for specific functions of software applications.

Now, let us understand how we perform mutation tests.

How to perform mutation test

The step to perform any particular software test varies depending on its objective and the software application's features. Unlike other software testing methods, the mutation testing method is quite different as it involves a slight change in the software application's source code and needs to execute the test that evaluates it. Here are the steps you can follow to perform the mutation test:

• Create test cases

A specific test case needs to be created for the functionality of software applications requiring mutation testing. You can run the test case against this software application.

• Create mutants

In the next step, you have to introduce small changes or faults in the software application's source code by developing many versions called mutants. Here, each mutant has specific single faults. Each mutant version is expected to fail, showing the test cases' effectiveness.

• Running of test cases

The created test cases are executed to the original and mutant software applications. However, you should be very careful, as test cases should be sufficient and modified to detect errors or bugs in software applications.

• Comparison of results

Comparing the results of both the original and mutant software applications. If the results of the original and mutant software applications are different, it is expected that created test cases can detect errors in the software application. Hence, the mutant can be killed by the test cases.

However, the results of the original and mutant software applications are the same. In that case, it is expected that the created test cases will not be able to detect errors in the software application. Hence, the mutant is kept alive.

• Repetition

You can repeat the same procedure for the remaining test cases of the test suite.

When you perform mutation testing, knowing the outcome of the test process is essential. To clarify this, you should know the outcomes of mutation testing. In the following section, we will look into the outcomes in brief.

Mutation testing outcomes

Mutation tests yield several distinct outcomes, including

• Mutant Program

Mutant programs naturally generate mutant programs. These mutant programs are designed to mimic our existing test cases for specific features in a software application. They're like mirrors reflecting the flaws that our test cases can uncover. These mutant programs are usually quite similar to the original ones, but they have subtle yet important differences. These differences help us improve the reliability of the software we're developing. It's like finding and fixing small issues to make our software better.

• Alive or Dead Mutant

Following the mutation tests, a mutation is classified as 'killed' or 'alive.' This designation indicates whether the tester (or their software) successfully identifies a coding issue. If a mutant remains 'alive,' it suggests potential shortcomings in the test cases.

• Mutation Test Case

The quality assurance team uses dedicated mutation-specific test cases to log information regarding their mutant programs. In simple terms, when you have a mutation test, you develop a mutation test case that can be considered an outcome but more a part of the mutation test. This test case ensures comprehensive documentation for each test, including intricate details about the mutations and their impact on the program.

• Mutation Score

The ultimate objective of any mutation test is to attain a mutation score of 100%. This signifies that the organization’s testing procedures have effectively located and eliminated every mutant. Thus, having a high mutation score is the best outcome of mutation testing.

With the above outcome, a mutation test can also uncover errors and bugs in the program code of the software applications.

Error detected 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:

• Ambiguous test cases

When mutation analysis shows a low mutation score (or any score below 100%), it indicates that the team's test cases may not adequately cover all potential errors that could impact a software application. 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.

• Inexperienced testing team

Mutation tests can also measure the team's capabilities, including their proficiency in detecting mutations and other flaws. If, in any case, testers are not able to detect mutants in the code despite having clear and detailed test cases. This could be because of the reason that testers are not able to apply these cases.

Mutant programs can highlight issues throughout the testing process, including examples of unskilled or untrained testers.

• Inadequate testing software

When a company uses these mutation tests to check its testing platforms or tools, it may discover that the software applications fail to accurately identify or eliminate mutant code. In response, the organization might explore alternative options until they find one compatible with their test cases. If the automation software applications cannot detect problematic code, it will likely encounter challenges in identifying other issues affecting the software.

• 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.

Mistakes in implementing mutation testing

Code mutation analysis is a procedure that necessitates careful implementation to prevent significant issues or oversights. Here, we outline seven pitfalls that testers should avoid when performing mutation tests:

• Incorrect scaling of mutations

In mutation analysis, considering the scale is important. This process aims to help testers identify errors within a software application. If the mutations are overly noticeable to testers, they may not effectively evaluate their ability to detect or address software issues.

• Invalid or functional mutations

Even when scaled appropriately, certain mutations may offer limited utility. For instance, if they do not trigger a fault or result in an issue that disrupts the application's functionality. Testers should be vigilant regarding how code alterations could impact the entire software.

• Incompatibility of test cases

For consistent and seamless testing, the test cases and mutations must align seamlessly. Whether selecting mutations to incorporate or designing initial test cases, the quality assurance team should ensure compatibility to facilitate a smoother testing process.

• Adherence to deadlines and schedules

Testing phases may vary in duration, but they should always stick to internal company deadlines. Organizations failing to schedule their mutation tests appropriately may struggle to complete the process on time. Therefore, the team should establish a comprehensive testing schedule before reaching the testing stage.

• Insufficient test coverage

While businesses may opt for random code mutations, covering a broad spectrum of potential issues remains essential. To ensure that both testers and the software can identify various mutants, the tests should encompass a minimum of value, decision, and statement mutations.

• Appropriate use of mutants

Mutation testing provides a fresh perspective on an application; however, it should exclusively serve as a tool to evaluate the effectiveness of the testing process. Companies must comprehend the precise capabilities and limitations of mutation testing, as it should complement other software checks.

• Excessive mutations

While it's crucial to ensure extensive test coverage, organizations should refrain from an excessive number of mutations. Each mutation program demands a significant amount of computational power, which can limit the number of simultaneous mutations an organization can perform. Overloading with mutations can also impact meeting testing deadlines.

Best Practice of mutation testing

Effective mutation testing involves several best practices that can enhance the quality and reliability of your software. These practices include comprehensive test case creation, mutation score tracking, and regular integration into your testing process. Below are some of the best practices in mutation analysis.

• Maximize the mutation score

The mutation score of a program denotes the percentage of mutants that a team or application can successfully detect and "terminate." For instance, if a round of mutation testing involves 40 mutants And testers discover 36 of them, the mutation score stands at 90%. The team's ultimate goal is always to achieve a perfect score of 100%.

• 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.

• Keep changes incremental

Mutated code should represent minor deviations from the original program. This approach illustrates how likely testers are to identify specific errors and demonstrates the software's sensitivity to minor coding issues. It is significant to maintain balance so that any minor change can show detectable errors.

• Limit mutations to one per program

Code mutation analysis assesses individual test cases in isolation to evaluate their thoroughness. To facilitate this, each mutated program should introduce only one modification from the original. Programs with multiple mutations may face challenges aligning effectively with test cases, as these mutations could potentially conflict.

• Carefully consider automation software

Many companies use code mutation to validate their team's use of automation software, ensuring it can identify errors as efficiently as a human tester. Consequently, selecting the appropriate automation platform is a critical consideration, as is exploring the potential for integrating robotic process automation.

• Using test-driven development (TDD)

With test-driven development, you mainly integrate the testing approach in every stage of software development. By leveraging TDD, you can ensure that test cases are compatible with the software application and pass the mutation test.

Conclusion

This tutorial has comprehensively discussed mutation testing, a crucial part of the testing strategy. Let us summarize the key learning from the above discussions. Mutation testing is a fault-driven approach that involves modification of the source code and subjecting it to test suites to uncover system faults. While historically perceived as time-consuming and costly, it has gained popularity in recent years due to the emergence of new automation tools. However, we know that no testing method is without challenges or limitations. As discussed in this tutorial, mutation testing testers are recommended to implement it accurately using its best practices.

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.

About Author

Nazneen Ahmad is an experienced technical writer with over five years of experience in the software development and testing field. As a freelancer, she has worked on various projects to create technical documentation, user manuals, training materials, and other SEO-optimized content in various domains, including IT, healthcare, finance, and education. You can also follow her on Twitter.