Immune and Evolutionary Approaches to Software Mutation Testing

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.

• Type: Conference
• Year: 2007
• Where: ICARIS
• Authors: Peter May, Jon Timmis, Keith Mander
• Keywords: Genetic Algorithm, ICARIS 2007, Immune Inspired Algorithm, Mutation Testing Adequacy