Abstract
Combinatorial test design (CTD) [1] is an effective test design technique, considered to be a testing best practice. CTD provides automatic test plan generation, but it requires a manual definition of the test space in the form of a combinatorial model, consisting of parameters, their respective values, and constraints on the value combinations. A valid test in the test space is defined to be an assignment of one value to each parameter that satisfies the constraints. A CTD algorithm automatically constructs a subset of the set of valid tests, termed a test plan, which covers all valid value combinations of every t parameters, where t is usually a user input. Such a test plan is said to achieve 100% t-way interaction coverage. A significant combinatorial reduction is achieved in the size of the resulting test plan (compared to manually designed test plans for example) because the tests generated by the CTD algorithm are very different from one another, maximizing their added value -- each of them covers as many unique t-way value tuples as possible. Note that tests produced by the algorithm are parameter-value assignments. Generating executable tests from them is often a separate, manual effort.
- D. R. Kuhn, R. N. Kacker, and Y. Lei. Introduction to Combinatorial Testing. Chapman & Hall/CRC, 2013. Google ScholarDigital Library
- R. E. Bryant. Graph-Based Algorithms for Boolean Function Manipulation. IEEE Trans. on Comp., 35(8):677--691, 1986. Google ScholarDigital Library
- R. Tzoref-Brill, P. Wojciak, and S. Maoz. Visualization of Combinatorial Models and Test Plans. In ASE, pages 144--154, 2016. Google ScholarDigital Library
- R. Tzoref-Brill and S. Maoz. Lattice-based semantics for combinatorial model evolution. In ATVA, pages 276--292, 2015.Google ScholarCross Ref
- R. Tzoref-Brill and S. Maoz. Syntactic and semantic differencing for combinatorial models of test designs. In Proceedings of the 39th International Conference on Software Engineering, ICSE, pages 621--631, 2017. Google ScholarDigital Library
- R. Tzoref-Brill and S. Maoz. Modify, enhance, select: Coevolution of combinatorial models and test plans. In Proceedings of the 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE, pages 235--245, 2018. Google ScholarDigital Library
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