One-test-at-a-time heuristic search for interaction test suites

Renée C. Bryce, Charles Colbourn

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

70 Scopus citations

Abstract

Algorithms for the construction of software interaction test suites have focussed on the special case of pairwise coverage; less is known about efficiently constructing test suites for higher strength coverage. The combinatorial growth of t-tuples associated with higher strength hinders the efficacy of interaction testing. Test suites are inherently large, so testers may not run entire test suites. To address these problems, we combine a simple greedy algorithmallwith heuristic search to construct and dispense one test at a time. Our algorithm attempts to maximize the number of t-tuples covered by the earliest tests so that if a tester only runs a partial test suite, they test as many t-tuples as possible.allHeuristic search is shown to provide effective methods for achieving such coverage.

Original languageEnglish (US)
Title of host publicationProceedings of GECCO 2007
Subtitle of host publicationGenetic and Evolutionary Computation Conference
Pages1082-1089
Number of pages8
DOIs
StatePublished - Aug 27 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

Other

Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
CountryUnited Kingdom
CityLondon
Period7/7/077/11/07

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Keywords

  • Covering arrays
  • Great flood
  • Heuristic search
  • Hill climbing
  • Simulated annealing
  • Software interaction testing
  • T-way interaction testing
  • Tabu search
  • Test suite prioritization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Bryce, R. C., & Colbourn, C. (2007). One-test-at-a-time heuristic search for interaction test suites. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 1082-1089). (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1276958.1277173