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A concept analysis inspired greedy algorithm for test suite minimization

Published:05 September 2005Publication History

ABSTRACT

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to ensure that changes to existing software do not break the software. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant as the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. In this paper, we present a new greedy heuristic algorithm for selecting a minimal subset of a test suite T that covers all the requirements covered by T. We show how our algorithm was inspired by the concept analysis framework. We conducted experiments to measure the extent of test suite reduction obtained by our algorithm and prior heuristics for test suite minimization. In our experiments, our algorithm always selected same size or smaller size test suite than that selected by prior heuristics and had comparable time performance.

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    • Published in

      cover image ACM Conferences
      PASTE '05: Proceedings of the 6th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
      September 2005
      118 pages
      ISBN:1595932399
      DOI:10.1145/1108792
      • cover image ACM SIGSOFT Software Engineering Notes
        ACM SIGSOFT Software Engineering Notes  Volume 31, Issue 1
        January 2006
        203 pages
        ISSN:0163-5948
        DOI:10.1145/1108768
        Issue’s Table of Contents

      Copyright © 2005 ACM

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      Publication History

      • Published: 5 September 2005

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      Overall Acceptance Rate57of159submissions,36%

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