Gary McGraw

Generating software test data by evolution

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.

Genetic algorithms for dynamic test data generation

In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, numerous attempts have been made to automate the process. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired software features. Often, the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. In contrast, combinatorial optimization techniques, such as those using genetic algorithms, are meant to solve difficult problems involving the simultaneous satisfaction of many constraints. In this paper, we discuss experiments with a test generation problem that is harder than the ones discussed in earlier literature-we use a larger program and more complex test adequacy criteria. We find a widening gap between a technique based on genetic algorithms and those based on random test generation.

Automated software test data generation for complex programs

We report on GADGET, a new software test generation system that uses combinatorial optimization to obtain condition/decision coverage of C/C++ programs. The GADGET system is fully automatic and supports all C/C++ language constructs. This allows us to generate tests for programs more complex than those previously reported in the literature. We address a number of issues that are encountered when automatically generating tests for complex software systems. These issues have not been discussed in earlier work on test-data generation, which concentrates on small programs (most often single functions) written in restricted programming languages.