An empirical study of regression test selection techniques

  • Authors:

  • Todd L. Graves;Mary Jean Harrold;Jung-Min Kim;Adam Porter;Gregg Rothermel

  • Affiliations:

  • Los Alamos National Lab, Los Alamos, NM;Georgia Institute of Technology, Atlanta;Univ. of Maryland, College Park;Univ. of Maryland, College Park;Oregon State Univ., Corvallis

  • Venue:
  • ACM Transactions on Software Engineering and Methodology (TOSEM)
  • Year:
  • 2001

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Abstract

Regression testing is the process of validating modified software to detect whether new errors have been introduced into previously tested code and to provide confidence that modifications are correct. Since regression testing is an expensive process, researchers have proposed regression test selection techniques as a way to reduce some of this expense. These techniques attempt to reduce costs by selecting and running only a subset of the test cases in a program's existing test suite. Although there have been some analytical and empirical evaluations of individual techniques, to our knowledge only one comparative study, focusing on one aspect of two of these techniques, has been reported in the literature. We conducted an experiment to examine the relative costs and benefits of several regression test selection techniques. The experiment examined five techniques for reusing test cases, focusing on their relative ablilities to reduce regression testing effort and uncover faults in modified programs. Our results highlight several differences between the techiques, and expose essential trade-offs that should be considered when choosing a technique for practical application.