Abstract
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 the search for new tests. The effectiveness of this algorithm is compared against an elitist Genetic Algorithm, with effectiveness measured by the number of mutant executions needed to achieve a specific mutation score. Results indicate that the Immune Inspired Approach is consistently more effective than the Genetic Algorithm, generating higher mutation scoring test sets in less computational expense.
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May, P., Timmis, J., Mander, K. (2007). Immune and Evolutionary Approaches to Software Mutation Testing. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_29
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DOI: https://doi.org/10.1007/978-3-540-73922-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73921-0
Online ISBN: 978-3-540-73922-7
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