Search-Based Test Data Generator for Data-Flow Dependencies Using Dominance Concepts, Branch Distance and Elitism

Abstract

Software testing is a complex and expensive activity of the software development life cycle. Software testing includes test data generation according to a test adequacy criterion. The use of search-based techniques has been the focus of researchers to automate the process of software test data generation for structural control-flow criteria. Automating test data generation remains a challenging problem for more robust adequacy criterion such as satisfying data-flow dependencies of a program. This study proposes a search-based approach that generates test data for data-flow dependencies of a program using dominance concepts, branch distance, and elitism. Genetic algorithm is used for the proposed approach and Gray encoding is used to encode test data. A set of subject programs is taken from the research literature to evaluate efficiency and effectiveness of the proposed approach. For the proposed approach, the measures considered are the mean number of generations and mean percentage coverage achieved. The performance of the proposed approach is evaluated by comparing the results with those of random search and earlier studies on data-flow testing. Over several experiments, it is shown that the proposed approach performed significantly better than random search and earlier studies with respect to data-flow test data generation and optimization. There is an increasing performance gap for more complex subject programs.