Comparative Analysis of Constraint Handling Techniques for Constrained Combinatorial Testing
| Autor: | Mark Harman, Huayao Wu, Changhai Nie, Yue Jia, Justyna Petke |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Dependency (UML) Computer science 020207 software engineering 02 engineering and technology Construct (python library) Constraint (information theory) Test case Software deployment 0202 electrical engineering electronic engineering information engineering Test suite Software system Tuple Software |
| Zdroj: | IEEE Transactions on Software Engineering. 47:2549-2562 |
| ISSN: | 2326-3881 0098-5589 |
| DOI: | 10.1109/tse.2019.2955687 |
| Popis: | Constraints depict the dependency relationships between parameters in a software system under test. Because almost all systems are constrained in some way, techniques that adequately cater for constraints have become a crucial factor for adoption, deployment and exploitation of Combinatorial Testing (CT). Currently, despite a variety of different constraint handling techniques available, the relationship between these techniques and the generation algorithms that use them remains unknown, yielding an important gap and pressing concern in the literature of constrained combination testing. In this article, we present a comparative empirical study to investigate the impact of four common constraint handling techniques on the efficiency of six representative (greedy and search-based) test suite generation algorithms. The results reveal that the Verify technique implemented with the Minimal Forbidden Tuple (MFT) approach is the fastest, while the Replace technique is promising for producing the smallest constrained covering arrays, especially for algorithms that construct test cases one-at-a-time. The results also show that there is an interplay between efficiency of the constraint handler and the test suite generation algorithm into which it is developed. |
| Databáze: | OpenAIRE |
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