What is White Box Testing? Techniques, Examples and Types

The significance of guaranteeing the quality and dependability of applications cannot be overstated in the fast-paced world of software development.

This is where White Box Testing comes in, a a powerful process that probes deeply into the inner workings of software to reveal possible faults and vulnerabilities.

By examining its different methods and examples, we will demystify the idea of white box testing in this extensive blog article.

Join us on this trip as we shed light on the many forms of White Box Testing and how they play a critical part in enhancing software quality and security—from comprehending the basic concepts to uncovering its practical implementations.

This article will provide you priceless insights into the realm of White Box Testing, whether you’re an experienced developer or an inquisitive tech enthusiast.

What is White Box Testing With an Example?

White Box Testing is also known by other names such as:

• Clear Box Testing
• Transparent Box Testing
• Glass Box Testing
• Structural Testing
• Code-Based Testing

White Box Testing is a software testing process that includes studying an application’s core structure and logic. It is carried out at the code level, where the tester has access to the source code and is knowledgeable of the internal implementation of the product.

White Box Testing, as opposed to Black Box Testing, which focuses on exterior behavior without knowledge of the underlying workings, tries to guarantee that the code performs as intended and is free of mistakes or vulnerabilities.

This testing method gives useful insights into the application’s quality and helps discover possible areas for development by studying the program’s structure, flow, and pathways.

In white box testing, the tester has to go through the code line by line to ensure that internal operations are executed as per the specification and all internal modules are properly implemented.

Example

Let’s consider a simple example of white box testing for a function that calculates the area of a rectangle:

def calculate_rectangle_area(length, width):
if length <= 0 or width <= 0:
return “Invalid input: Length and width must be positive numbers.”
else:
area = length * width
return area

Now, let’s create some test cases to perform white box testing on this function:

Test Case 1: Valid Input

• Input: length = 5, width = 3
• Expected Output: 15

Test Case 2: Invalid Input (Negative Value)

• Input: length = -2, width = 4
• Expected Output: “Invalid input: Length and width must be positive numbers.”

Test Case 3: Invalid Input (Zero Value)

• Input: length = 0, width = 6
• Expected Output: “Invalid input: Length and width must be positive numbers.”

Test Case 4: Valid Input (Floating-Point Numbers)

• Input: length = 4.5, width = 2.5
• Expected Output: 11.25

Test Case 5: Valid Input (Large Numbers)

• Input: length = 1000, width = 10000
• Expected Output: 10,000,000

In this situation, white box testing entails analyzing the function’s core logic and creating test cases to make sure all code paths are covered.

In order to determine if the function operates as intended in various contexts, test cases are designed to assess both valid and invalid inputs.

White box testing allows us to both confirm that the function is correct and find any possible defects or implementation problems.

White Box Testing Coverage

#1) Code level: Errors at the source code level, such as syntax mistakes, logical mistakes, and poor data handling, are found using white box testing.

#2) Branch and Path Coverage: By making sure that all potential code branches and pathways are checked, this testing strategy helps to spot places where the code doesn’t work as intended.

#3) Integration Issues: White box testing assists in identifying problems that may develop when several code modules are combined, assuring flawless system operation.

#4) Boundary Value Analysis: White box testing exposes flaws that happen at the boundaries of variable ranges, which are often subject to mistakes, by examining boundary conditions.

#5) Performance bottlenecks: By identifying regions of inefficient code and performance bottlenecks, engineers are better able to improve their product.

#6) Security issues: White box testing reveals security issues, such as errors in input validation and possible entry points for unauthorized users.

White box testing, sometimes referred to as clear box testing or structural testing, includes analyzing the software’s core logic and code. This testing technique may be used to find a variety of flaws and problems, including:

Code-level Errors: White box testing uncovers issues at the source code level, such as syntax errors, logical errors, and improper data handling.

Branch and Path Coverage: This testing approach ensures that all possible branches and paths within the code are tested, helping identify areas where the code doesn’t function as intended.

Integration Problems: White box testing aids in detecting issues that may arise when different code modules are integrated, ensuring seamless functioning across the entire system.

Boundary Value Analysis: By exploring boundary conditions, white box testing reveals bugs that occur at the limits of variable ranges, which are often prone to errors.

Performance Bottlenecks: It helps pinpoint performance bottlenecks and areas of inefficient code, allowing developers to optimize the software for better performance.

Security Vulnerabilities: White box testing exposes security vulnerabilities, such as input validation flaws and potential points of unauthorized access.

Difference Between White Box Testing and Black Box Testing

Both of them are two major classifications of software testing. They are very different from each other.

1. White box testing refers to the line by line testing of the code, while black box testing refers to giving the input to the code and validating the output.
2. Black box testing refers to testing the software from a user’s point of view, whereas the White box refers to the testing of the actual code.
3. In Black box testing, testing is not concerned about the internal code, but in WBT testing is based on the internal code.
4. Both the developers and testers use white box testing. It helps them validate the proper working of every line of the code.

Steps to Perform White Box Testing

Understand the Source Code:

A thorough knowledge of the source code is essential before beginning white box testing. To do this, examine the code base and note important modules, functions, and data structures. You may efficiently develop test cases that focus on particular aspects of the application by becoming familiar with the code.

Create Test Cases:

Create Test Cases: The foundation of white box testing is the creation of thorough test cases. Create test cases that cover numerous pathways, situations, and scenarios based on your understanding of the code. Your testing efforts will be guided by these test cases, ensuring that every facet of the code is carefully inspected.

When identifying test cases, you should consider the following:

* All possible paths through the code
* All boundary conditions (e.g., minimum and maximum values)
* All error conditions (e.g., invalid inputs)

Choose Testing Techniques:

A variety of testing methods are used in white box testing to evaluate code coverage. Statement coverage, branch coverage, path coverage, and condition coverage are some of these strategies. Select the right approaches to direct your testing strategy based on the amount of thoroughness necessary.

Perform Unit Testing:

To begin, conduct unit testing, which entails testing distinct components or modules. To verify their accuracy and find any defects or problems, each function or method should be evaluated using several inputs. The basis for guaranteeing the dependability of each code component is laid out through unit testing.

Execute Integration Testing:

Integration testing aims to evaluate the relationships between various components. Test for compatibility, data flow, and interactions when components are merged. This stage is essential for finding integration problems and making sure the application runs well.

Apply Coverage Analysis:

White box testing relies heavily on coverage analysis. To determine how much of the code your test cases cover, use coverage analysis tools. This gives you information about parts of the code that your tests do not cover, enabling you to improve coverage and reduce blind spots.

Use Boundary Value Analysis:

Use boundary value analysis to test the application with values at the outside limits of the input ranges. You can find flaws or problems relating to edge situations by testing extreme values. By doing this, you can make sure the program will function properly in many scenarios.

Implement Decision Testing:

Decision testing is designed to assess multiple outcomes of decision points—such as if-else and switch cases—within the code. Make careful you properly test each conceivable branch of the decision-making logic to identify any potential flaws or inconsistencies.

Evaluate Data Flow:

Analyze the data flow within the code to evaluate it. To guarantee data integrity and correct handling, test situations where data is transmitted across several code sections. This process is crucial for locating problems with data modification and transmission.

Run Regression Tests:

Once defects have been located and fixed, run tests to make sure the changes made did not result in any new problems or alter functionality. Regression testing protects against unforeseen effects of modifying the code.

Perform Code Reviews:

Work with developers and other team members to evaluate code. You may find problems early in the development process and improve overall code quality by exchanging ideas and feedback. Code reviews encourage group learning and development.

Document Results:

Results must be meticulously documented at every stage of the white box testing procedure. Record the test cases that were run, the level of coverage attained, the problems found, and the remedies implemented. This documentation offers a detailed account of your testing activities and results.

Iterate and Refine:

White box testing is an iterative process, so iterate and refine as necessary. Review your test cases, make any necessary updates, and carry out more testing as necessary when the code changes or evolves. The robustness and resilience of the code are maintained through this iterative process.

Collaborate:

Throughout the white box testing process, effective communication and collaboration with the development team are essential. Exchange ideas, disseminate research, and collaborate to raise the caliber and usefulness of the codebase.

Types of White Box Testing/Techniques used in White Box Testing

The term “white box testing,” also known as “clear box testing” or “structural testing,” refers to a variety of testing methods, each with a particular emphasis on a distinct element of the core logic and code of the product. The primary categories of White Box Testing are as follows:

Statement coverage testing

During the testing process, this approach seeks to test every statement in the source code at least once.

Branch coverage testing

Testing for branches or decision points is known as branch coverage, and it makes sure that every branch or decision point in the code is tested for both true and false outcomes.

Path coverage testing

Path coverage testing examines all potential statements or statement combinations in the code to ensure thorough code execution.

Condition coverage testing

The goal of condition coverage testing, commonly referred to as “decision coverage,” is to make sure that all potential outcomes of boolean conditions inside the code are evaluated at least once. This method aids in ensuring that every choice or branch in the code gets executed on its own.

Example:

Loop Coverage Testing

Testing loops in the code to make sure all conceivable iterations are carried out is the subject of the loop coverage testing approach.

Let’s consider an example of loop coverage testing using a simple program that calculates the factorial of a given number using a for loop:

In this example, the ‘factorial’ function calculates the factorial of a given number using a ‘for’ loop. Loop coverage testing aims to test different aspects of loop behavior. Here are the scenarios covered:

Test case 1: Calculating the factorial of 5. The loop runs from 1 to 5, multiplying result by 1, 2, 3, 4, and 5. The expected result is 120.

Test case 2: Calculating the factorial of 3. The loop runs from 1 to 3, multiplying result by 1, 2, and 3. The expected result is 6.

Test case 3: Calculating the factorial of 0. Since the loop’s range is from 1 to 0+1, the loop doesn’t execute, and the function directly returns 1.

Boundary Value Analysis

It evaluates how the program behaves at the border between acceptable and unacceptable input ranges.

Let’s consider an example of a function that calculates the cost of shipping a package based on its weight:

Data flow testing

Data flow testing looks at how data moves through the program and confirms that data variables are handled correctly.

Control flow testing:

Control flow testing analyzes the order of the statements in the code or the control flow.

Let’s consider a simple example of a control flow testing scenario using a function that calculates the discount for a shopping cart based on the total amount spent:

In this example, the ‘calculate_discount’ function calculates the discount based on the total amount spent. Control flow testing aims to test different paths through the function’s code, including the if-elif-else branches. Here are the scenarios covered:

Test case 1: The total amount is 150. It satisfies the condition total_amount >= 100, so the discount should be 0.1 (10%).

Test case 2: The total amount is 80. It doesn’t satisfy the first condition but satisfies the second condition total_amount >= 50. Hence, the discount should be 0.05 (5%).

Test case 3: The total amount is 30. It doesn’t meet any of the conditions, so the discount remains 0 (no discount).

Testing using Decision Tables

Based on predetermined criteria, decision tables are used to test different combinations of inputs and their related outputs.

Let’s consider an example of a decision table for determining the eligibility of a loan based on income and credit score:

Example: Loan Eligibility Decision Table

In this example, the decision table has four conditions: “Income Range,” “Credit Score Range,” and “Eligibility.” The different possible values for each condition are represented in rows. The “Eligibility” column indicates whether the applicant is eligible for a loan based on the given conditions.

For instance, if an applicant has a high income and a good credit score, they are eligible for a loan according to the decision table (C7). Conversely, if an applicant has a low income and a poor credit score, they are not eligible (C3).

Testing using a decision table involves creating test cases that cover all the different combinations of conditions and their expected outcomes. Here are a few example test cases:

1. Applicant with low income and good credit score:

• Income Range: Low
• Credit Score Range: Good
• Expected Eligibility: Not Eligible

2. Applicant with moderate income and fair credit score:

• Income Range: Moderate
• Credit Score Range: Fair
• Expected Eligibility: Eligible

3. Applicant with high income and poor credit score:

• Income Range: High
• Credit Score Range: Poor
• Expected Eligibility: Not Eligible

By systematically testing each combination of conditions based on the decision table, we can ensure that the application behaves as expected for different scenarios and helps in verifying that the defined rules are correctly implemented.

Mutation Testing

In order to determine how well the test suite is able to identify these alterations, mutation testing includes making minor modifications or mutations to the code.

These numerous White Box Testing approaches are used to test the underlying logic of the program thoroughly and achieve varying degrees of code coverage. Depending on the complexity of the product and the testing goals, testers may combine various approaches.

Top White Box Testing Tools

#1) Veracode

Veracode is a prominent toolkit that helps in identifying and resolving the defects quickly, economically and easily. It supports various programming languages like .NET, C++, JAVA, etc. It also supports security testing.

#2) EclEmma

EclEmma is a free Java code coverage tool. It has various features that ease the testing process. It is widely used by the testers to conduct white box testing on their code.

#3) JUnit

JUnit is a widely-used testing framework for Java that plays a crucial role in automating and simplifying the process of unit testing. It provides a platform for developers to write test cases and verify their Java code’s functionality at the unit level. JUnit follows the principles of test-driven development (TDD), where test cases are written before the actual code implementation.

#4) CppUnit:

CppUnit is a testing framework for C++ that was created to facilitate unit testing for C++ programs. It is based on the design concepts of JUnit. It allows programmers to create and run test cases to verify the accuracy of their C++ code.

#5) Pytest

This C++ test framework by Google has an extensive list of features including test Discovery, Death tests, Value-parameterized tests, fatal & non-fatal failures, XML test report generation, etc. It supports various platforms like Linux, Windows, Symbian, Mac OS X, etc.

Advantages of White Box Testing

• Code optimization
• Transparency of the internal coding structure
• Thorough testing by covering all possible paths of a code
• Introspection of the code by the programmers
• Easy test case automation

Disadvantages of White Box Testing

• A complex and expensive procedure
• Frequent updating of the test script is required whenever changer happens in the code
• Exhaustive testing for large-sized application
• Not always possible to test all the conditions.
• Need to create a full range of inputs making it a very time-consuming process

Conclusion

White box testing is a predominantly used software testing technique. It is based on evaluating the code to test which line of the code is causing the error. The process requires good programming language skills and is generally carried out by both developers and testers.

FAQs

#1) How can developers ensure adequate code coverage when performing White Box Testing?

By using a variety of techniques, developers may guarantee proper code coverage in White Box Testing.

They should start by clearly defining test goals and requirements, making sure that all crucial features are included. It is best to write thorough test cases that cover all potential outcomes, including boundary values and error handling.

To track the amount of code that is exercised by tests, code coverage tools like Jacoco or Cobertura may be utilized. It is crucial to remedy low-coverage regions by adding test cases or making adjustments after regularly analyzing code coverage metrics to do so.

To carry out thorough testing effectively, test automation should be used, and branch and path coverage guarantees that all potential choices and code routes are checked.

Working together with the QA team ensures thorough integration of White Box and Black Box Testing. Developers may improve code quality and lower the chance of discovering bugs by adhering to certain best practices.

#2) What are some best practices for conducting effective White Box Testing in a development team?

effective White Box research Following various best practices is necessary while testing in a development team. Here are a few crucial ones:

Clear needs: As this information informs test case creation, make sure that the team has a complete grasp of the functional and non-functional needs for the project.

Comprehensive Test Cases: Create detailed test cases that cover all possible code pathways, decision points, and boundary conditions. This will guarantee complete code coverage.

Code reviews: It should be conducted on a regular basis to guarantee code quality, spot possible problems, and confirm that tests are consistent with code changes.

Test Automation: Use test automation to run tests quickly and reliably, giving you more time for exploratory testing and a lower risk of human mistake.

Continuous Integration: Include testing in the process to spot problems before they become serious and to encourage routine code testing.

Test Data Management: To achieve consistent and reproducible test findings, test data should be handled with care.

Code coverage : metrics should be regularly monitored in order to identify places with poor coverage and focus testing efforts there.

Collaboration with QA Team: FCollaboration with QA Team: Encourage cooperation between the QA team and the developers to make sure that all White Box and Black Box Testing activities are coordinated and thorough.

Regression testing: Regression testing should be continuously carried out to ensure that new code modifications do not cause regressions or affect working functionality.

Documentation: Test cases, processes, and results should all be well documented in order to encourage team collaboration and knowledge sharing.