Combinatorial testing has been shown to be a very effective testing strategy. An important problem in combinatorial testing is dealing with constraints, i.e., restrictions that must be satisfied in order for a test to be valid. In this paper, we present an efficient algorithm, called IPOG-C, for constraint handling in combinatorial testing. Algorithm IPOG-C modifies an existing combinatorial test generation algorithm called IPOG to support constraints. The major contribution of algorithm IPOG-C is that it includes three optimizations to improve the performance of constraint handling. These optimizations can be generalized to other combinatorial test generation algorithms. We implemented algorithm IPOG-C in a combinatorial test generation tool called ACTS. We report experimental results that demonstrate the effectiveness of algorithm IPOG-C. The three optimizations increased the performance by one or two orders of magnitude for most subject systems in our experiments. Furthermore, a comparison of ACTS to three other tools suggests that ACTS can perform significantly better for systems with more complex constraints.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.