Immune and evolutionary approaches to software mutation testing

Pete May*, Jon Timmis, Keith Mander

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

20 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Immune Systems - 6th International Conference, ICARIS 2007, Proceedings
PublisherSpringer Nature
Number of pages12
ISBN (Print)3540739211, 9783540739210
Publication statusPublished - 2007
Event6th International Conference on Artificial Immune Systems, ICARIS 2007 - Santos, Brazil
Duration: 26 Aug 200729 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4628 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Conference on Artificial Immune Systems, ICARIS 2007
Period26 Aug 200729 Aug 2007


  • genetic algorithm
  • clonal selection
  • adequacy criterion
  • clonal selection algorithm
  • mutation score


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