A Multi-Objective Genetic Algorithm to Test Data Generation

Evolutionary testing has successfully applied search based optimization algorithms to the test data generation problem. The existing works use different techniques and fitness functions. However, the used functions consider only one objective, which is, in general, related to the coverage of a testing criterion. But, in practice, there are many factors that can influence the generation of test data, such as memory consumption, execution time, revealed faults, and etc. Considering this fact, this work explores a multiobjective optimization approach for test data generation. A framework that implements a multi-objective genetic algorithm is described. Two different representations for the population are used, which allows the test of procedural and object-oriented code. Combinations of three objectives are experimentally evaluated: coverage of structural test criteria, ability to reveal faults, and execution time.