Immune and evolutionary approaches to software mutation testing

Authors:
Pete May;Jon Timmis;Keith Mander
Affiliations:
Computing Laboratory, University of Kent, Canterbury, Kent, UK;Departments of Computer Science and Electronics, University of York, York, UK;Computing Laboratory, University of Kent, Canterbury, Kent, UK
Venue:
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Year:
2007

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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.