Combinatorial Test Generation for Multiple Input Models with Shared Parameters
Chang Rao, Nan Li, Yu Lei, Jin Guo, YaDong Zhang, Raghu N. Kacker, D. Richard Kuhn
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.
, Li, N.
, Lei, Y.
, Guo, J.
, Zhang, Y.
, Kacker, R.
and Kuhn, D.
Combinatorial Test Generation for Multiple Input Models with Shared Parameters, IEEE Transactions on Software Engineering, [online], https://doi.org/10.1109/TSE.2021.3065950, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931133
(Accessed December 7, 2022)