Commonly known as all-pair testing, pairwise testing is a combinatorial method of software testing that for each pair of input parameter to a system, tests all possible discrete combinations of those parameters. It is a test design technique that delivers hundred percent test coverage. The most common bugs in a program are usually found and triggered either by an input parameter or by an interaction between pair of parameters. Bugs involving interactions between three or more parameters are both progressively less common as well as progressively more expensive to find, such testing has as its limit the testing of all possible inputs. Hence, pairwise testing technique is immensely useful in designing test for applications involving multiple parameters. Its test suit covers all combinations and therefore, it is not exhaustive yet very effective in finding bugs.

We will first try to find out the number of cases using the conventional software testing technique. We can consider the list box values as 0 and others as 0 is neither positive nor negative. Radio button and check box values cannot be reduced, so each one of them will have 2 combinations (ON or OFF). The Text box values can be reduced into three inputs (Valid Integer, Invalid Integer, Alpha-Special Character). The effectiveness of any software testing technique depends on whether test settings corresponding to the actual faults are included in the test sets. When test sets do not include settings corresponding to actual faults, the faults may not be detected.

Pairwise testing relies on all pairs of parameters interacting to determine the results, and sometimes, this assumption may be false. Incorrect assumptions like this can prevent bugs from being caught in testing. In this section, we will be discussing some easy-to-use, free, and popular combinatorial testing tools. Use Case testing is a functional testing method that verifies the software’s behavior based on real-life scenarios or use cases. Testers create test cases derived from use cases specified during the software requirements analysis phase. Once you have the generated test cases, you can also automate them via some black box testing tool like Testsigma.

Method of generating software test cases using the experimental design (in Japanese)

You can find more information on the research behind ACTS and more of its features here. The tester would then execute this test case, following the steps and verifying that the application meets the expected results. If any discrepancies are found, the tester would report them as defects to be fixed by the development team. Once you have identified the parameters you want to test, you can use a pairwise tool to generate test cases.

Conversely, we can be confident that the software works correctly for t-way combinations contained in passing tests. When the tests are derived from t-way CAs, we know that 100% of the t-way combinations have been tested. Since combinatorial testing follows a complex procedure and it can be a tedious task to manually perform this testing on many input parameters, we, therefore, use combinatorial testing tools. Not only are these tools easy to use with many input parameters, but they can also add constraints in the input parameters and generate test configurations accordingly. There are numerous tools available on the internet to perform combinatorial testing.

Another intuitive tool for performing combinatorial testing is testcover.com where factors, values, and constraints are simply written in the editor, and test configurations are generated. This tool has an extremely fast and efficient algorithm and can generate about 15 test cases in 1 second. Another advantage is the tool’s easy generator requests where we just have to write the factors and values in new lines, that’s it! CA algorithms can produce highly compact matrices of t-way value combinations, but how does a tester decide what values to use? This approach is known as combinatorial testing (CT), an extension of the established field of statistical Design of Experiments (DoE).

You would repeat this process for all of the other pairwise test cases. Richard Kuhn is a computer scientist in the Computer Security Division of the National Institute of Standards and Technology (NIST). He has authored or coauthored 2 books and more than 100 publications on information security, empirical studies of software failure, and software assurance, and is a senior member of the IEEE. Before joining NIST, he worked as a systems analyst with NCR Corporation and the Johns Hopkins University Applied Physics Laboratory. He received an MS in Computer Science from the University of Maryland, College Park, and an MBA from the College of William & Mary.

Pairwise Testing

It operates on the principle that a majority of defects are caused by interactions between two parameters. By concentrating on testing all pairs of input values, this method uncovers defects that could be overlooked by other testing techniques. The idea behind pairwise testing is to test every possible pair of inputs. Pairwise testing reduces the number of test cases required to cover all possible combinations of input parameters, making the testing process more efficient and effective.

• With the NASA application, for example, 67% of the failures were triggered by only a single parameter value, 93% by 2-way combinations, and 98% by 3-way combinations.
• These qualities and features in the software systems has inaugurated a demand of software and applications that are uniquely designed and have innovative as well as creative features.
• For example, when testing the length of a line segment, pairwise testing would involve testing all combinations of line lengths, such as 1-2, 1-3, 1-4, 2-3, 2-4, and 3-4.
• Thus, the distribution of faults in this initial testing is quite close to the distribution of conditions documented in the FAA report.
• This can reduce the time and effort required for testing and the number of test cases that need to be written.
• Nowadays, software systems are diverse as well as complex and have many possible configurations.

Matrices known as covering arrays (CAs) cover all t-way combinations of variable values, up to a specified level of t (typically t ≤ 6), making it possible to efficiently test all such t-way interactions. A question naturally arises as to why the fault profiles of different applications are somewhat similar. While there is no definitive answer as yet, one clue can be seen in Fig. Figure 3A shows the distribution of conditions in branching statements (e.g., if, while) in four large avionics software modules [11].

This can help ensure that the software functions as expected and that users are not experiencing unexpected issues or errors. Using carefully chosen test vectors, this can be done much faster than an exhaustive search of all combinations of all parameters, by “parallelizing” the tests of parameter pairs. Combinatorial testing has rapidly gained favor among software testers in the past decade as improved algorithms have become available and practical success has been demonstrated. Significant advances have occurred in algorithm performance, and the critical area of constraint representation and processing. Pairwise testing aims to ensure that all possible discrete combinations of inputs have been tested without the need to test every single combination.

Use case testing ensures that the software application meets its intended objectives, and all scenarios are tested before releasing the software. In summary, the appropriate use of these testing methods, among others, guarantees software quality, reduces costs, and ensures customer satisfaction. Pairwise testing is helpful when testing complex systems that have multiple input parameters and multiple possible values for each parameter. It can significantly reduce the number of test cases that need to be created while ensuring that all possible discrete combinations of parameters are tested. This can help reduce test case creation time and cost and improve the software’s overall quality.

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This means that all possible combinations of line lengths are being tested to ensure the accuracy of the length measurement. For example, the length of a line segment could be 1 unit, 2 units, 3 units, or 4 units, so all possible combinations of these lengths must be tested to ensure accuracy. This allows for detecting any discrepancies or errors in the measurement of the line segment. This tool gives a bit more functionality and features as compared to the previous tool. Firstly, it allows us to create multiple input parameter models instead of just one like in the previous tool. Also, we can save these models as well and export them to a different type of files.

Studies by other researchers [8], [9], [10] have been consistent with these results. Computer scientists and mathematicians both work on algorithms to generate pairwise test suites. Numerous exist to generate such test suites as there is no efficient exact solution for every possible input and constraints scenarios. Combinatorial testing is a method of software testing that for https://www.globalcloudteam.com/ some input parameters to a system (typically, a software algorithm), tests possible discrete combinations of those parameters. The idea behind it is that system failures can result from the interaction of two or more parameters. To achieve high quality and end-user’s satisfaction, testers employ various testing methods to identify and resolve defects in software applications.

In this article, we will delve into the details of these two methods, exploring their benefits, techniques, and tools. This type of testing is usually performed manually and requires an organized approach to systematically explore all possible combinations of input parameters and document any bugs or issues encountered. Pairwise testing tools automate the test case design process by generating a compact set of parameter value choices as the desired test cases. This is done by applying the all-pairs testing technique, which involves testing all possible combinations of two parameters.