The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.12 - December (2001 vol.27)
pp: 1085-1110
ABSTRACT
<p>This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function. In our work, the function is minimized by using one of two genetic algorithms in place of the local minimization techniques used in earlier research. We describe the implementation of our GA-based system and examine the effectiveness of this approach on a number of programs, one of which is significantly larger than those for which results have previously been reported in the literature. We also examine the effect of program complexity on the test data generation problem by executing our system on a number of synthetic programs that have varying complexities.</p>
INDEX TERMS
Software testing, automatic test case generation, code coverage, genetic algorithms, combinatorial optimization
CITATION
C.C. Michael, G. Mcgraw, M.A. Schatz, "Generating Software Test Data by Evolution", IEEE Transactions on Software Engineering, vol.27, no. 12, pp. 1085-1110, December 2001, doi:10.1109/32.988709
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool