Combinatorial Test Generation for Multiple Input Models With Shared Parameters

• Published in: IEEE transactions on software engineering Vol. 48; no. 7; pp. 2606 - 2628
• Main Authors: Rao, Chang, Li, Nan, Lei, Yu, Guo, Jin, Zhang, Yadong, Kacker, Raghu N., Kuhn, D. Richard
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; IEEE Computer Society
• Subjects: Combinatorial analysis; Combinatorial testing; Information science; Mathematical models; multiple input models; Optimization; Parameters; Presses; Redundancy; Schedules; shared parameters; T-way test generation; Test pattern generators; Test sets; Testing
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2021.3065950

Combinatorial testing typically considers a single input model and creates a single test set that achieves <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3065950.gif"/> </inline-formula>-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 <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3065950.gif"/> </inline-formula>-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.