Generating software test data by evolution

TLDR

Genetic algorithms are used to automatically generate software test data by framing the problem as a function‑minimization task, extending prior dynamic test data generation research. The study implements a GA‑based test data generator and evaluates its effectiveness on several programs, including one significantly larger than those previously reported. The method employs two genetic algorithms to minimize the test data generation function, implements the GA system, and tests its performance across programs of varying sizes, including synthetic programs with differing complexities.

Abstract

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.