TDSGen: An Environment Based on Hybrid Genetic Algorithms for Generation of Test Data

Abstract

The software testing has gained importance and can be considered a fundamental activity to ensure the quality of the software being developed. In the literature, there are three groups of testing techniques proposed to reveal a great number of faults with minimal effort and costs: functional technique, structural technique and fault-based technique. These techniques are generally associated to testing criteria. A criterion is a predicate to be satisfied to consider the testing activity ended and the program tested enough [3]. The criterion generally requires the exercising of certain elements of the source code (decision statement) called required elements. To satisfy a testing criterion (coverage of 100%), it is necessary to provide input data to execute paths that exercise all the required elements. This is a very hard task because it is not possible its complete automatization due to several testing limitations.