Improving test suites for efficient fault localization
Proceedings of the 28th international conference on Software engineering
Sequence diagram to colored Petri nets transformation testing: an immune system metaphor
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Example-based model-transformation testing
Automated Software Engineering
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In this paper, we present several complementary computational intelligence techniques that we explored in the field of .Net component testing. Mutation testing serves as the common backbone for applying classical and new artificial intelligence (AI) algorithms. With mutation tools, we know how to estimate the revealing power of test cases. With AI, we aim at improving automatically test cases efficiency. So, we looked first at genetic algorithms (GA) to solve the problem of test. The aim of the selection process is to generate test cases able to kill as many mutants as possible. Then, we propose a new AI algorithm that fits better to the test optimization problem we called bacteriological algorithm (BA): BAs behave better that GAs for this problem . However, between GAs and BAs, a family of intermediate algorithms exists: we explore the whole spectrum of these intermediate algorithms to determine whether an algorithm exists that would be more efficient than BAs: the approaches are compared on a .Net system.