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An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization

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  • Ning Li
  • Rui Wang
  • Yu-li Tian
  • Wei Zheng

Abstract

During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy.

Suggested Citation

  • Ning Li & Rui Wang & Yu-li Tian & Wei Zheng, 2016. "An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, April.
  • Handle: RePEc:hin:jnlmpe:5813490
    DOI: 10.1155/2016/5813490
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    Cited by:

    1. Chaitanya Kanchibhotla & Somayajulu D. V. L. N. & Radha Krishna P., 2022. "A Parallel Particle Swarm Optimization for Community Detection in Large Attributed Graphs," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-23, January.

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