Combinatorial Test Generation for Multiple Input Models with Shared Parameters.
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- Record sourced from PubMed, PMID 39697973.
- Also identified by DOI 10.1109/tse.2021.3065950 and PMC identifier 11653414.
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Abstract
Combinatorial testing typically considers a single input model and creates a single test set that achieves <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>t</mi></math> -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 <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>t</mi></math> -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.