Factorials Experiments, Covering Arrays, and Combinatorial Testing

Kacker, Raghu N.; Kuhn, D. Richard; Lei, Yu; Simos, Dimitris E.

doi:10.1007/s11786-021-00502-7

Cited by 3 publications

(2 citation statements)

References 106 publications

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“…Varying the intensity of the covering arrays can be used to detect these various interactions efficiently. This can provide various covering strengths to distinct groupings of variables, and thus can provide a realistic way to evaluate real applications [13] , [14].…”

Section: Combinatorial Testingmentioning

confidence: 99%

Combinatorial Testing Approaches: A Systematic Review

2022

IJCCCE

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Testing is a vital phase in software development, and having the right amount of test data is an important aspect in speeding up the process. As a result of the integrationist optimization challenge, extensive testing may not always be practicable. There is also a shortage of resources, expenses, and schedules that impede the testing process. One way to explain combinational testing (CT) is as a basic strategy for creating new test cases. CT has been discussed by several scholars while establishing alternative tactics depending on the interactions between parameters. Thus, an investigation into current CT methods was started in order to better understand their capabilities and limitations. In this study, 97 publications were evaluated based on a variety of criteria, including the generation technology, test strategy method, supported interactions, mixed coverage ,and support constraints between parameters. CT analysis had a wide range of interaction assistance options available to researchers. Since 2010, a unified interaction has been the most common style of interaction between the two parties. The year 2018 was hailed as the most successful in terms of CT by researchers. Researchers should focus on one test at a time and metaheuristic search strategies for t-way CT. There has also been a significant increase in the popularity of other trends, such as deep learning (DL). CT appears to be a useful testing technique for balancing and fault detection capabilities for a variety of systems and applications, according to our research. Future research and software development may benefit from this information. Index Terms— Combinatorial Testing, Test Case Generation, Optimization Algorithms, Software Testing, Artificial Intelligent.

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Section: Combinatorial Testingmentioning

confidence: 99%

Combinatorial Testing Approaches: A Systematic Review

2022

IJCCCE

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“…The application of automated software testing tools has found thousands of new bugs-many of them critical from a reliability or security point of view-in a lot of different application domains, including, but not limited to, embedded systems, application programming interfaces (APIs), and web and mobile applications. In recent years, a new field in the domain of software testing has opened up, successfully applying combinatorial methods for testing purposes [10]. The methodological origins of this field, which is termed combinatorial testing (CT) for software [11], can be traced to the field of Design of Experiments (DoE) within statistics [12], with the latest achievements and future directions of CT being outlined in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Sequences for Disaster Scenario Generation

Garn¹,

Kieseberg²,

Schreiber³

et al. 2023

Oper. Res. Forum

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Training exercises are an important tool in crisis management, as they can assist in a multitude of tasks, such as planning pre-crisis resource requirements and allocation, response planning and help train emergency personnel for actual crises. To be effective, the exercises have to utilize well constructed scenarios and be able to replicate certain characteristics of a crisis situation. In this paper, we propose a conceptual mathematical modeling approach for the automated generation of scenarios for disaster exercises via certain combinatorial sequence structures. The derived scenarios within an exercise collectively fulfill different notions of combinatorial sequence coverage, thereby providing the means to test existing response strategies for deficiencies as well as to train emergency personnel for their ability to handle different arrangements of events. This guaranteed diversity by construction can be used as a basis to obtain quantitative assurance statements when these scenarios have been successfully mastered by participants in exercises. We illustrate our proposed approach utilizing two different combinatorial structures for two example disasters.

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