Automatic test-data generation: an immunological approach

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3 Citations (Scopus)

Abstract

In previous research, we presented an approach to automatically generate test-data for object-oriented software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow coverage. The experimental results from testing six Java classes helped us identify a number of problematic test targets, and suggest that in the future full data-flow coverage with a reasonable computational cost may be possible if we overcome these obstacles. To this end, the investigation of Artificial Immune System (AIS) algorithms was chosen. This paper provides a brief summary of our previous work and an introduction to both Human and Artificial Immune System. We then suggest a framework for the application of AIS algorithms to the problem of automated testing, followed by some thoughts on why and how these algorithms can be beneficial in our effort to improve the performance of our previously implemented GA. Finally, our preliminary results from a proof-of-concept implementation are presented.
Original languageEnglish
Title of host publicationTAIC PART 2007 - Testing Academic and Industrial Conference - Practice and Research Techniques
Subtitle of host publicationProceedings: CO-LOCATED WITH MUTATION 2007
Place of PublicationLos Alamitos
PublisherIEEE
Pages77-81
Number of pages4
ISBN (Print)9780769529844
DOIs
Publication statusPublished - 1 Sep 2007
EventTesting - Academic and Industrial Conference - Windsor, United Kingdom
Duration: 12 Sep 200714 Sep 2007

Conference

ConferenceTesting - Academic and Industrial Conference
CountryUnited Kingdom
CityWindsor
Period12/09/0714/09/07

Keywords

  • automatic test-data generation
  • immunological approach
  • genetic algorithm
  • GA

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  • Cite this

    Liaskos, K., & Roper, M. (2007). Automatic test-data generation: an immunological approach. In TAIC PART 2007 - Testing Academic and Industrial Conference - Practice and Research Techniques : Proceedings: CO-LOCATED WITH MUTATION 2007 (pp. 77-81). IEEE. https://doi.org/10.1109/TAIC.PART.2007.24