Black box testing is defined as a software testing methodology in which the tester analyzes application functionality without a thorough knowledge of its internal design. Conversely, white box testing is defined as a software testing methodology in which the tester’s knowledge of the application’s internal workings is leveraged during testing. A detailed explanation and the critical differences between black box and white box testing are covered in this article.
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Black box testing is a software testing methodology in which the tester analyzes the functionality of an application without a thorough knowledge of its internal design. Conversely, in white box testing, the tester is knowledgeable of the internal design of the application and analyzes it during testing.
The term black box symbolizes a black outer covering of the application, preventing testers from seeing its inner workings and compelling them to examine only the end-user experience. Likewise, the term white box signifies the application’s transparency, allowing the tester to see through the outer box and into the inner code.
Before we dive into the key differences between these two software testing methodologies, let’s take an in-depth look at their definitions.
In black box testing, the testing team analyzes the workings of an application without first having an extensive understanding of its internal structure and design. During testing, the input value is simply compared with the output value. Due to its nature, black box testing is sometimes called specification-based testing, closed box testing, or opaque box testing.
Black box testing mainly focuses on the comprehensive examination of application functionality. It is closely related to behavioral testing; however, behavioral testers may have limited knowledge of internal application workings.
The black box methodology is used to test most modern software applications. It covers numerous test cases, allowing maximum bugs to be discovered. This testing method is used at all stages of the software development cycle.
Black box testing focuses on understanding user experience, which means testers do not require in-depth technical knowledge to carry it out. It’s a valuable way to provide extensive testing coverage, especially compared to white box testing, which is sometimes so precise that testers miss the bigger picture.
This form of testing takes place post-completion of development, and both processes are independent.
The software application’s internal coding, design, and structure are examined in white box testing to verify data flow from input to output. White box testing is leveraged to improve design, usability, and application security. The other names for this methodology include code-based testing, glass box testing, open box testing, clear box testing, and transparent box testing.
Unlike black box testing, which focuses on ensuring a smooth user experience, white box testing is intensive. Combined with other bug-quashing techniques, it is a robust quality assurance tool. This methodology is designed to conduct in-depth simulations of all the scenarios the application might encounter at the code level.
The granularity offered by white box testing is an effective way to crush bugs. This transparent and rigorous testing approach also gives insights into all the possible outputs the application can theoretically generate. White box testing is leveraged to spot hidden internal errors and optimize code.
QA teams usually subject every facet of an application through black box testing procedures. However, white box testing is generally arranged solely for the most critical components of an application. This is due to the resource-intensive nature of white box procedures. It is deployed for applications such as payment remittance and national securityâ€“features that have the potential to affect living conditions directly and, thus, cannot afford to fail.
Both black box testing and white box testing have advantages and disadvantages of their own, and certain defects can only be detected using a mix of the two methodologies.
Let’s examine the three primary distinctions between the two software testing approaches.
|Black Box Testing||White Box Testing|
|The black box testing procedure promotes a rigorous and comprehensive approach to application analysis. The goal is quality assurance for the end user.
The process begins with the testing team understanding the requirement statement of the application. This step generally requires the presence of a well-documented software requirement specification.
Testing methodologies such as equivalence partitioning and boundary value analysis are used to determine sets of valid inputs and their predicted outputs.
This information is then used to build test cases, which are then run to check whether they are successful or not.
The actual outcomes are compared against the intended outcomes, and failed test cases are classified as bugs or defects.
These bugs are reported to the development team, who then work on fixing them.
Once the developers update the application, the testing team retests the previously reported problem areas and checks if they have been fixed.
|The white box testing process is much more â€˜surgical’ than black box testing and far more effective on smaller targets. The goal is to assess all the possible cases and scenarios for the target, which is often a â€˜too-critical-to-fail’ application, component, or functionality.
The first step is identifying the target: the component, feature, or application to be tested. By choosing a narrow target, white box testers can be thorough and ensure flawless functionality. This is usually achieved by picking the smallest possible logical module, working on it and then moving on to the next one.
Of course, one can also execute white box testing on larger systems; however, this is often a resource-intensive process and should only be done if the need is greater than the effort.
The next step is creating a flow graph by plotting all possible scenarios. This helps determine the scope of the testing exercise and write relevant test cases. Scenarios must account for user journeys, program specifications, use cases, technical specifications, and pseudocode.
Once the flow graph is prepared, all the paths the journey might take must be mapped for testing and framed as test cases.
Finally comes the execution phase, where the testing is completed, and any issues are recorded for fixing.
|Black Box Testing||White Box Testing|
|The below-listed techniques are used in black box testing:
Equivalence partitioning: Also known as equivalence class partitioning (ECP), this black box testing technique calls for the input values for the application or system to be classified based on outcome similarity.
This enables testing teams to use only one value from within the class or group for analyzing the outcome rather than having to review all the relevant input values from the group. This technique maintains test coverage, and the amount of rework required and time spent are minimized.
For instance, let’s assume a text field that only accepts numerical inputs between 1950 and 2000 to be tested. Using equivalence partitioning, we can create three sets of groups for testing this field: any number less than 1950, any number between 1950 and 2000, and any number greater than 2000.
The valid class will be any one number between 1950 and 2000, while the other two groups will be invalid classes.
In this example, the testing team has reduced the cases to only three, allowing all possible scenarios to be tested within a few moments.
Boundary value analysis: In this technique, the tester focuses on analyzing both valid and invalid values at the boundariesâ€“or the limits at which the system behavior changes. This technique is popular as many applications exhibit bugs when processing boundary values.
Continuing the above example, let’s say a field that accepts values between 1950 and 2000 is being tested. The tester, using boundary value analysis, will enter the following values during testing: 1949 (1950-1), 1950, 1951 (1950+1), 1999 (2000-1), 2000, and 2001 (2000+1).Â
Decision table testing: With this method, the tester detects two outputs for two conditions that are defined by a logical connection, such as:
(Condition = True)
then output1 ;
else output2; /*(condition = False)*/
Based on the above example, the tester will create a decision table with all the probable outcomes.
State transition testing: In this technique, the tester analyzes the various states of the application, which change as per events or conditions that the application is subjected to. Each event triggers a state that is then treated as a scenario to be tested.
This technique is handy for simple scenarios, while more complex scenarios will create complicated transition diagrams and make the technique less effective.
Experience-based testing: Guessing the errors that might arise in the application is a key example of this technique. Here, the testing team leverages its experience around the application’s behavior and its functionalities to find the areas prone to errors.
Examples of guessing based on experience include searching for common errors, such as dividing by zero, handling null values in text fields, and accepting blank inputs where they should not be allowed.
Graph-based testing: All applications are created using objects. This method identifies the objects, and an â€˜object graph’ is created. This graph is used to identify all object relationships, write test cases, and test for error discovery.
Comparison testing: Finally, in this technique, independent versions of one application are compared to each other during the testing process.
|The below-listed techniques are used in white box testing:
Statement coverage: All statements are at least once executed at the source code level in this white box testing approach. The number of statements executed is recorded.
This technique allows the testing team to analyze the source code and set expectations around what it can and cannot do. It is also reliable for checking code quality and verifying path flow. However, one cannot use it to test false conditions.
This method tests the internal coding and infrastructure of the software. It can be carried out by programmers and is suitable for Drupal specialists.
Decision coverage: In this technique, decision coverage is given to Boolean values, and Boolean expressions’ true and false results are reported.
Control flow statements that create a possibility of two or more outcomes, such as â€˜do while’, â€˜if’, and â€˜case’, are considered as â€˜decision points’ in this technique as they can lead to two outcomes: either true or false.
This technique covers all the possible results of every Boolean condition within the code through control flow charts and graphs. However, it can sometimes be difficult to achieve complete coverage due to the existence of complicated expressions.
Branch coverage: This technique covers all branches of the control flow graph. Each decision point condition’s possible true and false outcomes are covered at least once. This method guarantees that the several branches of each decision point are executed successfully.
Numerous methods are available for calculating branch coverage, the most common of which is pathfinding. This method uses the number of executed branch paths to calculate branch coverage. Developers can substitute decision coverage with branch coverage.
Condition coverage: In this technique, the testing team analyzes all the conditional expressions in an application for every possible outcome. All the conditions are tested independently, and all the possible results of every condition are tested at least once. Predicate coverage is another name for this technique.
Multiple condition coverage: In this technique, all the possible permutations of condition outcomes in every decision, as well as all entry points, are tested at least once. Full multiple condition coverage generally requires a high number of test cases.
Path coverage: All program paths are tested in this comprehensive technique. It is leveraged to ensure that every application path is utilized at least once. This technique is generally useful for testing complex programs.
Control flow testing: This technique determines the sequence in which an application’s statements or instructions are executed via a control structure. The test cases of the analyzed application are developed using the control structure. Control flow testing is generally used during unit testing.
Testers, using this technique, select a specific part of the application for setting the testing path. The control flow graph is created from the edge, node, junction node, and decision node to outline all the possible execution paths.
Data flow testing: Finally, white box testers use this technique to analyze the control flow of programs to explore the variable sequence based on the event sequence. During testing, the focus lies on two points: where values are assigned to the variables and where these values are used.
In this technique, the control flow graph is used to detect logical inconsistencies that disrupt data flow. Reasons for anomaly detection include variables being used without initialization and initialized variables not being used.
|Black Box Testing||White Box Testing|
|The main goal of black box testing is to ensure the system works flawlessly for the end user.
The base of black box testing is built on external assumptions. The tester does not take into consideration the underlying behavior of the application.
This testing methodology suits higher-level testing activities, including acceptance and system testing.
The testing team need not have in-depth knowledge of programming to execute this testing methodology, nor does it need expertise in implementation.
One cannot automate black box testing easily because the testing and programming functions work closely together.
The basis for test cases is the software requirement specification document. Testing generally begins after preparing the requirement specification paper, and involves the developer, tester, and end user.
The methodology is sweeping in nature, focusing on trial and error and a lack of granularity. On the plus side, it is less resource-intensive.
The source code is not accessed during black box testing, making this an ineffective way of testing algorithms. However, it helps test lengthy portions of code.
Testing expertise is not mandatoryâ€“testers with lower skill levels can still carry out the black box testing of applications, even without knowledge of the operating system or programming language.
|The main goal of white box testing is to ensure that an application’s code scores high in quality and integrity.
An in-depth understanding of the system’s internal workings serves as the cornerstone of white box testing. This enables the tester to evaluate the functioning and design of the code.
This testing methodology suits lower-level testing activities, including integration and unit testing.
A working grasp of programming knowledge is required for white box testing. The tester must also be knowledgeable about implementation.
White box testing is simple to automate.
The basis for test cases is the detailed design document. Once the document is prepared, testing involves programmers, developers, and specialist testers.
This testing methodology is superior in terms of granularity; however, this comes at the cost of higher resource-intensiveness.
Developers can use this exhaustive testing style to analyze internal bounds and data domains. It is also well-suited for testing algorithms.
The QA team accesses the code during white box testing and, as a result, can remove flawed or unnecessary lines of code that might serve as liabilities in the future. However, the downside of code accessibility is the risks associated with outsourcingâ€“it is possible to steal code during testing.
Finally, white box testing requires high levels of experience and expertise.
The main distinctions between black box testing and white box testing have been examined in this article. While both testing methods have their advantages and drawbacks, they are each best suited for specific testing scenarios. They can spot bugs and improve system quality, whether used independently or cohesively.
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