In this article, we will discuss a few such tools that are available for free on the internet to generate test configurations. This example demonstrates how pairwise testing significantly reduces the number of test cases while maintaining adequate test coverage of the interactions between input parameters. For example, a program may work correctly when you input two numbers but may cause an error when you input two different numbers. By testing all possible combinations of parameters, the tester can identify these types of errors. Pairwise testing is especially useful when testing complex software with many parameters. By testing all possible combinations of parameters, the tester can ensure that no errors occur in any combination.

Feng Duan is a PhD student in Department of Computer Science and Engineering at the University of Texas at Arlington. His research interests include software analysis and testing, particularly combinatorial testing, model-based testing and input parameter modeling. Let’s say you have an application that allows users to enter two numbers, and the application will output the sum of the two numbers. You can use pairwise testing to test all possible combinations of two numbers, such as (1, 2), (2, 3), (3, 4), (4, 5), etc. By testing all the combinations of two numbers, you can be sure that the application is working correctly and will not fail when given different numbers. Raghu Kacker is a senior researcher in the Applied and Computational Mathematics Division of NIST.

Apart from these, the clients and users also demand exponential performance, functionality, quality, scalability, effectiveness, and more. Moreover, it can also cause bodily harm, which can result in large economic losses or security breaches, as well as affect the quality of day-to-day life. However, software testers often have limited time and budget, which frequently makes it impossible to exhaustively test software. Additionally, testers often intuitively test for defects that they anticipate, while less foreseen defects are overlooked and not tested. The best way to overcome such a situation is to implement Combinatorial Testing in software and applications.

Constructions and Combinatorial Problems in Design of Experiments

In this section, we discuss challenges to generating combinatorial test suites, categories of algorithms that have been popular in literature, and automated tools for this purpose. One more example is where Pairwise testing can also test relationships between two variables. Further, pairwise testing may not be the most effective technique in cases where the order of parameters is important, as this type of testing does not account for ordering. Laleh Ghandehari is a Computer Science PhD candidate at the University of Texas at Arlington.

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.

• Pairwise testing tools are especially useful for testing systems with many parameters, as they can significantly reduce the number of test cases that need to be executed.
• It operates on the principle that a majority of defects are caused by interactions between two parameters.
• The idea behind it is that system failures can result from the interaction of two or more parameters.
• Pairwise testing tools automate the test case design process by generating a compact set of parameter value choices as the desired test cases.
• For example, you could generate a set of test cases covering all possible combinations of browsers and operating systems.

This can reduce the time and effort required for testing and the number of test cases that need to be written. The main advantage of Pairwise testing is that it enables the tester to identify errors that may occur when two or more different parameters are combined. This is important as errors may occur in combinations of parameters that are not tested individually. Pairwise testing, using tests that cover all 2-way combinations of parameter values, has long been accepted as a way of detecting such interaction failures [1], [2], [3], [4]. For instance, consider a failure that is triggered by unusual combinations of three or four sensor values.

A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing

For thorough checking, it may be necessary to test 3-way and 4-way combinations of values. The question arises as to whether testing all 4-way combinations is enough to detect all errors. What is the distribution of interaction failures beyond 2-way in real systems? Surprisingly, this question had not been studied when NIST began investigating interaction failures in 1999 [5].

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).

Software fault interactions and implications for software testing

Combinatorial Testing is an approach that can systematically examine system setting in a manageable number of tests and by systematically covering t-way interactions. Therefore, here is an expounded discussion on Combinatorial Testing, to define its significance, methods and other major qualities. Pairwise testing, also referred to as all-pairs testing, is a combinatorial testing technique that focuses on testing all possible combinations of input parameters with a minimal number of 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.

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.

He has contributed to design and evaluation of industrial experiments, quality engineering, and evaluation of measurement uncertainty in physical measurements and in outputs of computational models. His current interests include development and use of combinatorial https://www.globalcloudteam.com/ mathematics methods for testing software systems. He is a Fellow of the American Statistical Association and a Fellow of the American Society for Quality. He has also worked in industry (AT&T Bell Telephone Laboratories) and academia (Virginia Tech, Blacksburg, VA).

This helps ensure that the software works correctly in all scenarios, which is essential for producing reliable and bug-free software. It is based on covering all possible discrete combinations of input parameters by using a minimum set of test cases. This technique is helpful when the number of input parameters is large, as it helps to reduce test execution time and cost. After successful use for a long period of time, the software is installed in a new location, with a different user base, and a new set of bugs appear. Typically, the reason is not surprising—the change in usage has resulted in a different set of inputs, and some of these input combinations trigger failures that have escaped previous testing and extensive use. Such failures are known as interaction failures, because they are only exposed when two or more input values interact to cause the program to reach an incorrect result.

Nearly all are single conditions or 2-way, with a rapidly declining proportion involving 3-way or more complex sets of conditions. This curve is superimposed on the fault profiles presented above in Fig. Note that it closely matches the profile for the NASA database application. The data for this application were from initial testing results, while the other curves are for fielded products. Thus, the distribution of faults in this initial testing is quite close to the distribution of conditions documented in the FAA report.

Another advantage of this tool is that it can use three different algorithms (FIPOG, FIPOG-F, FIPOG-F2) to generate the combinatorial object array. So if we specify a degree of interaction of 2, ACTS will give us test sets that cover all the possible combinations of values for all the above pairs of parameters. In this example, the two variables being tested are a circle’s size (radius) and the circumference of that same circle. To test this relationship, pairwise testing would involve testing all combinations of circle sizes (radius) and corresponding circumference values. As mentioned in previous sections, CT for the purpose of software testing stems from a rich history of DoE, including designs such as OAs [12]. This previous work has had a strong influence on algorithms to generate combinatorial test suites.