Immune and evolutionary approaches to software mutation testing
作者： Pete May , Jon Timmis , Keith Mander
DOI: 10.1007/978-3-540-73922-7_29
关键词:
摘要: We present an Immune Inspired Algorithm, based on CLONALG, for software test data evolution. Generated tests are evaluated using the mutation testing adequacy criteria, and used to direct search new tests. The effectiveness of this algorithm is compared against elitist Genetic with measured by number mutant executions needed achieve a specific score. Results indicate that Approach consistently more effective than generating higher scoring sets in less computational expense.
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