Published: March 17, 2021
Citation: IEEE Transactions on Software Engineering vol. 48, no. 7, (July 2022) pp. 2606-2628
Author(s)
Chang Rao (Chongqing Jiaotong University), Nan Li (Dassault Systems), Yu Lei (UTA), Jin Guo (Southwest Jiaotong University), Yadong Zhang (Southwest Jiaotong University), Raghu Kacker (NIST), Richard Kuhn (NIST)
Combinatorial testing typically considers a single input model and creates a single test set that achieves \(t\) -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy \(t\) -way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.
Combinatorial testing typically considers a single input model and creates a single test set that achieves \(t\) -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient...
See full abstract
Combinatorial testing typically considers a single input model and creates a single test set that achieves \(t\) -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy \(t\) -way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.
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Keywords
combinatorial testing; T-way test generation; multiple input models; shared parameters
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