Combinatorial Test Generation for Multiple Input Models With Shared Parameters

Combinatorial testing typically considers a single input model and creates a single test set that achieves t 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 ap...

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Bibliographic Details
Published in:IEEE transactions on software engineering 2022-07, Vol.48 (7), p.2606-2628
Main Authors: Rao, Chang, Li, Nan, Lei, Yu, Guo, Jin, Zhang, Yadong, Kacker, Raghu N., Kuhn, D. Richard
Format: Article
Language:English
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
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Summary:Combinatorial testing typically considers a single input model and creates a single test set that achieves t 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 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.
ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2021.3065950