Application of Markov Chain Monte Carlo Random Testing to Test Case Prioritization in Regression Testing

• ZHOU Bo

  Department of Computer Science and Engineering, University of California Riverside

• OKAMURA Hiroyuki

  Department of Information Engineering, Graduate School of Engineering, Hiroshima University

• DOHI Tadashi

  Department of Information Engineering, Graduate School of Engineering, Hiroshima University

Abstract

This paper proposes the test case prioritization in regression testing. The large size of a test suite to be executed in regression testing often causes large amount of testing cost. It is important to reduce the size of test cases according to prioritized test sequence. In this paper, we apply the Markov chain Monte Carlo random testing (MCMC-RT) scheme, which is a promising approach to effectively generate test cases in the framework of random testing. To apply MCMC-RT to the test case prioritization, we consider the coverage-based distance and develop the algorithm of the MCMC-RT test case prioritization using the coverage-based distance. Furthermore, the MCMC-RT test case prioritization technique is consistently comparable to coverage-based adaptive random testing (ART) prioritization techniques and involves much less time cost.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E95.D (9), 2219-2226, 2012

  The Institute of Electronics, Information and Communication Engineers

Keywords

• regression testing
• test case prioritization
• random testing
• Markov chain Monte Carlo

Details

• CRID: 1390001204379657600
• NII Article ID: 10031142837
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e95.d.2219
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E95.D/9/E95.D_2219/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed