Automatic mutation test input data generation via ant colony

Kamel Ayari, Salah Bouktif, Giuliano Antoniol

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    100 Citations (Scopus)


    Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.

    Original languageEnglish
    Title of host publicationProceedings of GECCO 2007
    Subtitle of host publicationGenetic and Evolutionary Computation Conference
    Number of pages8
    Publication statusPublished - 2007
    Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
    Duration: Jul 7 2007Jul 11 2007

    Publication series

    NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference


    Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
    Country/TerritoryUnited Kingdom


    • Ant colony optimization
    • Mutation testing
    • Search based testing
    • Test input data generation

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Theoretical Computer Science


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