Abstract
The combination of testing techniques is considered an effective strategy to evaluate the quality of a software product. However, the selection of which techniques to combine in a software project has been an interesting challenge in the software engineering field because the high number of techniques available at the technical literature. This paper presents an approach developed to support the combined selection of model-based testing techniques, applying multiobjective combinatorial optimization strategies, by determining the minimum dominating set in a bipartite and bi-weighted graph. Thus, an evolutionary strategy based on a multiobjective genetic algorithm is proposed to generate trade-off techniques subsets between the maximum coverage of software project characteristics and the minimum eventual effort to construct models used for test cases generation. In an empirical evaluation, our evolutionaryalgorithmstrategygavebetterresultsthanthepreviousapproaches.
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da Silva Grande, A., Neto, A.C.D., de Freitas Rodrigues, R. (2012). Providing Trade-Off Techniques Subsets to Improve Software Testing Effectiveness: Using Evolutionary Algorithm to Support Software Testing Techniques Selection by a Web Tool. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_23
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DOI: https://doi.org/10.1007/978-3-642-34459-6_23
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