Automatic Test Data Generation Using a Genetic Algorithm

Abstract

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic search techniques offer much promise in regard to these problems. Metaheuristic search techniques are high-level frameworks, which utilize heuristics to seek solutions for combinatorial problems at a reasonable computational cost. In this paper, we present a new evolutionary approach for automated test data generation for structural testing. Our method presents several noteworthy features: It uses a newly defined program modeling allowing an easy program manipulation. Furthermore, instead of affecting a unique value for each input variable, we assign to each input an interval. This representation has the advantage of delimiting first the input value and to refine the interval progressively. In this manner, the search space is explored more efficiently. We use an original fitness function, which expresses truthfully the individual quality. Furthermore, we define a crossover operator allowing to effectively improving individuals.