21 February 2024
11 Min. Read
What Is White Box Testing: Techniques And Examples
White Box Testing, also known as Clear, Glass, or Open Box Testing, is a software testing method in which the internal structure, design, and coding of the software are known to the tester. This knowledge forms the basis of test cases in White Box Testing, enabling a thorough examination of the software from the inside out.
Unlike Black Box Testing, which focuses on testing software functionality without knowledge of its internal workings, White Box Testing delves deep into the code to identify hidden errors, verify control flow and data flow, and ensure that internal operations are performed as intended.
What is White Box Testing?
The primary aim of White Box Testing is to enhance security, improve the design and usability of the software, and ensure the thorough testing of complex logical paths. Testers, who are often developers themselves or specialized testers with programming knowledge, use this method to execute paths through the code and test internal structures of applications.
This method is essential for identifying and rectifying potential vulnerabilities at an early stage in the software development lifecycle, thus saving time and resources in the long run.
By understanding the intricacies of how the application works from within, testers can create more effective test scenarios that cover a wide range of use cases and conditions, leading to a more reliable, secure, and high-quality software product. Through its comprehensive and detailed approach, White Box Testing plays a crucial role in the development of software that meets stringent quality standards.
💡 Get close to 90% coverage in under a sprint i.e. 2 weeks. More about it here.What is the process of White Box Testing?
The process of White Box Testing involves several technical steps, designed to thoroughly examine the internal structures of the application. It is a detailed and systematic approach that ensures not just the functionality, but also the robustness and security of the software. Here’s a step-by-step approach incase you want to proceed with white box testing:
1. Understanding the Source Code
The first step is to gain a deep understanding of the application's source code. This involves reviewing the code to comprehend its flow, dependencies, and the logic it implements.
2. Identify Testable Paths
Once the code is understood, testers identify the testable paths. This includes all possible paths through the code, from start to end. The aim is to cover as many paths as possible to ensure comprehensive testing.
Example: Consider a simple function that calculates a discount based on the amount of purchase. The function might have different paths for different ranges of purchase amounts.
def calculate_discount(amount):
if amount > 1000:
return amount * 0.1 # 10% discount
elif amount > 500:
return amount * 0.05 # 5% discount
else:
return 0 # no discountIn this example, there are three paths to test based on the amount: → greater than 1000,
→ greater than 500 but less or equal to 1000, and
→ 500 or less.
3. Develop Test Cases
With the paths identified, the next step is to develop test cases for each path. This involves creating input data that will cause the software to execute each path and then defining the expected output for that input.
Example Test Cases for the calculate_discount function:
Test Case 1:
amount = 1500 (expects a 10% discount, so the output should be 150)
Test Case 2:
amount = 700 (expects a 5% discount, so the output should be 35)
Test Case 3:
amount = 400 (expects no discount, so the output should be 0)
💡 A FinTech Company With Half a Million Users Achieved Over 90% Code Coverage Without Writing Any Test Cases, Read It Here.4. Execute Test Cases and Monitor
Test cases are then executed, and the behavior of the software is monitored closely. This includes checking the actual output against the expected output, but also observing the software's state to ensure it behaves as intended throughout the execution of each path.
5. Code Coverage Analysis
An important part of White Box Testing is code coverage analysis, which measures the extent to which the source code is executed when the test cases run. The goal is to achieve as close to 100% code coverage as possible, indicating that the tests have examined every part of the code.
6. Review and Debug
Any discrepancies between expected and actual outcomes are reviewed. This step involves debugging the code to find and fix the root causes of any failures or unexpected behavior observed during testing.
7. Repeat as Necessary
The process is iterative. As code is added or modified, White Box Testing is repeated to ensure that new changes do not introduce errors and that the application remains consistent with its intended behavior.
Example: Unit Testing with a Framework
Unit testing frameworks (e.g., JUnit for Java, PyTest for Python) are often used in White Box Testing to automate the execution of test cases. Here's an example using PyTest for the calculate_discount function:
import pytest
# The calculate_discount function defined earlier
@pytest.mark.parametrize("amount,expected", [
(1500, 150),
(700, 35),
(400, 0),
])
def test_calculate_discount(amount, expected):
assert calculate_discount(amount) == expectedThis code defines a series of test cases for calculate_discount and uses PyTest to automatically run these tests, comparing the function's output against the expected values.
White Box Testing is a powerful method for ensuring the quality and security of software by allowing testers to examine its internal workings closely. Through careful planning, execution, and analysis, it helps identify and fix issues that might not be apparent through other testing methods.
Types of White Box Testing
White Box Testing, with its unique approach of peering into the very soul of the software, uncovers a spectrum of testing types, each designed to scrutinize a specific aspect of the code's inner workings.
This journey through the types of White Box Testing is akin to embarking on a treasure hunt, where the treasures are the bugs hidden deep within the layers of code.
1. Unit Testing
Unit testing is akin to testing the bricks of a building individually for strength and integrity. It involves testing the smallest testable parts of an application, typically functions or methods, in isolation from the rest of the system.
Example: Consider a function that checks if a number is prime:
def is_prime(number):
if number <= 1:
return False
for i in range(2, int(number**0.5) + 1):
if number % i == 0:
return False
return TrueA unit test for this function could verify that it correctly identifies prime and non-prime numbers:
def test_is_prime():
assert is_prime(5) == True
assert is_prime(4) == False
assert is_prime(1) == False2. Integration Testing
Integration testing examines the connections and data flow between modules or components to detect interface defects. It's like testing the strength of mortar between bricks.
Example: If a system has a module for user authentication and another for user profile management, integration testing would verify how these modules interact, for instance, ensuring that a user's login status is correctly shared and recognized across modules.
💡HyperTest builds tests that tests your service with all dependencies like downstream services, queues and database. Schedule a demo now to learn more3. Path Testing
Path testing dives deep into the possible routes through a given part of the code. It ensures that every potential path is executed at least once, uncovering hidden bugs that might only emerge under specific conditions.
Example: For the is_prime function, path testing involves creating test cases that cover all paths through the function: checking numbers less than or equal to 1, prime numbers, and non-prime numbers.
4. Loop Testing
Loop testing focuses on validating all types of loops within the code, ensuring they function correctly for all possible iterations. This includes testing loops with zero, one, multiple, and boundary number of iterations.
Example: If we add a function to calculate factorial using a loop:
def factorial(n):
result = 1
for i in range(1, n + 1):
result *= i
return resultLoop testing would involve testing with n=0 (should return 1), n=1 (should return 1), and a higher value of n (e.g., n=5, should return 120).
5. Condition Testing
Condition testing scrutinizes the decision-making logic in the code, testing every possible outcome of Boolean expressions.
Example: In a function that determines if a year is a leap year:
def is_leap_year(year):
return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0)Condition testing would involve testing years that are divisible by 4 but not 100, years divisible by 100 but not 400, and years divisible by 400.
6. Static Code Analysis
Unlike the dynamic execution of code in other types, static code analysis involves examining the code without running it. Tools for static analysis can detect potential vulnerabilities, such as security flaws or coding standard violations.
Example: Tools like Pylint for Python can be used to analyze the is_prime function for code quality issues, such as naming conventions, complexity, or even potential bugs.
White Box Testing Techniques
1. Statement Coverage
Statement Coverage involves executing all the executable statements in the code at least once. This technique aims to ensure that every line of code has been tested, but it does not guarantee the testing of every logical path.
Example: Consider a simple function that categorizes an age into stages:
def categorize_age(age):
if age < 13:
return 'Child'
elif age < 20:
return 'Teen'
elif age < 60:
return 'Adult'
else:
return 'Senior'Statement coverage would require tests that ensure each return statement is executed at least once.
2. Branch Coverage (Decision Coverage)
Branch Coverage extends beyond statement coverage by ensuring that each decision in the code executes in all directions at least once. This means testing both the true and false outcomes of each if statement.
Example with the categorize_age function: To achieve branch coverage, tests must be designed to cover all age ranges, ensuring that each condition (if and elif) evaluates to both true and false.
3. Condition Coverage
Condition Coverage requires that each Boolean sub-expression of a decision statement is evaluated to both true and false. This technique digs deeper than branch coverage by examining the logical conditions within the decision branches.
Example: If a function decides eligibility based on multiple conditions:
def is_eligible(age, residency_years):
return age > 18 and residency_years >= 5Condition coverage would involve testing the combinations that make each condition (age > 18 and residency_years >= 5) true and false.
4. Path Coverage
Path Coverage aims to execute all possible paths through the code, which includes loops and conditional statements. This comprehensive technique ensures that every potential route from start to finish is tested, uncovering interactions and dependencies between paths.
Example: For a function with multiple conditions and loops, path coverage would require creating test cases that traverse every possible path, including all iterations of loops and combinations of conditions.
5. Loop Coverage
Loop Coverage focuses specifically on the correctness and behavior of loop constructs within the code. It tests loops with zero iterations, one iteration, multiple iterations, and boundary conditions.
Example: Consider a loop that sums numbers up to a limit:
def sum_to_limit(limit):
sum = 0
for i in range(1, limit + 1):
sum += i
return sumLoop coverage would test the function with limit values of 0 (zero iterations), 1 (one iteration), a moderate number (multiple iterations), and a high number close to potential boundary conditions.
6. MC/DC (Modified Condition/Decision Coverage)
MC/DC requires each condition in a decision to independently affect the decision's outcome. This technique is particularly valuable in high-integrity systems where achieving a high level of confidence in the software's behavior is crucial.
Example: For a function with a complex decision:
def process_application(age, income, credit_score):
if age > 18 and (income > 30000 or credit_score > 600):
return 'Approved'
else:
return 'Denied'MC/DC would involve testing scenarios where changing any single condition changes the outcome of the decision, ensuring independent testing of each condition's impact on the decision.
Tools To Perform White Box Testing
White Box Testing, an integral part of software development, is supported by a myriad of tools designed to automate and simplify the process. These tools offer various features to assist developers and testers in ensuring their code is not only functional but also robust and secure. Among the plethora of options, certain tools stand out for their unique capabilities and offerings.
1. HyperTest - Tool To Perform White Box Testing:
HyperTest marks its presence in the realm of White Box Testing with its cutting-edge approach to testing and debugging. It is designed to significantly reduce the time and effort involved in the testing process, employing advanced algorithms to automate complex testing tasks.
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Key Features:
Advanced Test Generation: Automatically generates test cases to maximize code coverage, ensuring a thorough examination of the software.
Real-time Bug Detection: Identifies and reports bugs in real-time, allowing for immediate action and resolution.
Integration Capabilities: Seamlessly integrates with continuous integration/continuous deployment (CI/CD) pipelines, enhancing the efficiency of development workflows.
Pricing: HyperTest operates on a subscription-based model, although specific pricing details are often tailored to the needs of the organization.
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2. Coverity - Tool To Perform White Box Testing:
Coverity by Synopsys offers a sophisticated static code analysis tool that enables developers to identify and fix bugs and security vulnerabilities within their codebase.
Key Features:
Static Application Security Testing (SAST): Identifies security vulnerabilities and quality issues in code without executing it.
Seamless Integration: Easily integrates with popular IDEs and CI/CD pipelines, facilitating a smooth workflow.
Comprehensive Codebase Analysis: Offers support for a wide range of programming languages and frameworks.
Pricing: Coverity provides a tailored pricing model based on the size of the organization and the scope of the project.
3. Parasoft C/C++test - Tool To Perform White Box Testing:
Parasoft's solution is tailored for C and C++ development, offering both static and dynamic analysis capabilities to improve code quality and security.
Key Features:
Static Code Analysis: Detects potential code flaws and vulnerabilities early in the development cycle.
Unit Testing: Facilitates the creation and execution of unit tests, including test case generation and code coverage analysis.
Compliance Reporting: Supports compliance with industry standards such as MISRA, AUTOSAR, and ISO 26262.
Pricing: Parasoft C/C++test offers customized pricing based on the specific needs of the business.
4. WhiteHat Security - Tool To Perform White Box Testing:
WhiteHat Security specializes in application security, offering solutions that encompass White Box Testing among other security testing methodologies.
Key Features:
Sentinel Source: Provides static code analysis to identify vulnerabilities in web applications.
Integration with Development Tools: Integrates with popular development and CI/CD tools for streamlined workflows.
Detailed Vulnerability Reports: Offers detailed explanations of vulnerabilities, including risk assessment and remediation guidance.
Pricing: Pricing for WhiteHat Security's solutions is customized based on the scale of the application and the level of service required.
Conclusion
As we reach the conclusion of our exploration into the realm of White Box Testing and the diverse array of tools designed to navigate its complexities, it's clear that the choice of tool can significantly influence the effectiveness, efficiency, and thoroughness of your testing process.
Among the standout options, HyperTest emerges not just as a tool but as a comprehensive solution, poised to transform the landscape of software testing through its innovative approach and advanced capabilities.
HyperTest distinguishes itself by offering an unparalleled blend of speed, automation, and depth in testing that aligns perfectly with the goals of White Box Testing. Its ability to generate detailed test cases automatically ensures that every nook and cranny of your code is scrutinized, maximizing code coverage and uncovering hidden vulnerabilities that might otherwise go unnoticed. This level of thoroughness is crucial for developing software that is not only functional but also robust and secure against potential threats.
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