On-demand test suite reduction

  • Authors:

  • Dan Hao;Lu Zhang;Xingxia Wu;Hong Mei;Gregg Rothermel

  • Affiliations:

  • Peking University, China / Key Laboratory of High Confidence Software Technologies, China;Peking University, China / Key Laboratory of High Confidence Software Technologies, China;Peking University, China / Key Laboratory of High Confidence Software Technologies, China;Peking University, China / Key Laboratory of High Confidence Software Technologies, China;University of Nebraska, USA

  • Venue:
  • Proceedings of the 34th International Conference on Software Engineering
  • Year:
  • 2012

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Abstract

Most test suite reduction techniques aim to select, from a given test suite, a minimal representative subset of test cases that retains the same code coverage as the suite. Empirical studies have shown, however, that test suites reduced in this manner may lose fault detection capability. Techniques have been proposed to retain certain redundant test cases in the reduced test suite so as to reduce the loss in fault-detection capability, but these still do concede some degree of loss. Thus, these techniques may be applicable only in cases where loose demands are placed on the upper limit of loss in fault-detection capability. In this work we present an on-demand test suite reduction approach, which attempts to select a representative subset satisfying the same test requirements as an initial test suite conceding at most l% loss in fault-detection capability for at least c% of the instances in which it is applied. Our technique collects statistics about loss in fault-detection capability at the level of individual statements and models the problem of test suite reduction as an integer linear programming problem. We have evaluated our approach in the contexts of three scenarios in which it might be used. Our results show that most test suites reduced by our approach satisfy given fault detection capability demands, and that the approach compares favorably with an existing test suite reduction approach.