Generating software test data by evolution

• Published in: IEEE transactions on software engineering Vol. 27; no. 12; pp. 1085 - 1110
• Main Authors: Michael, C.C., McGraw, G., Schatz, M.A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2001; IEEE Computer Society
• Subjects: Algorithms; Automatic testing; Complexity; Computer programs; Dynamic tests; Evolution; Genetic algorithms; Heuristic algorithms; Instruments; Mathematical functions; Minimization methods; Optimization; Performance evaluation; Simulated annealing; Software; Software engineering; Software testing; Studies; System testing; Systems design; Testing; United States > US
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/32.988709

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.