An Automatic Software Test-Data Generation Scheme Based on Data Flow Criteria and Genetic Algorithms

Software test-data generation research primarily focuses on using control flow graphs for producing an optimum set of test cases. This paper proposes the integration of a data flow graph module with an existing testing framework and the utilisation of a specially designed genetic algorithm for automatically generating test cases based on data flow coverage criteria. The enhanced framework aims to explore promising aspects of software testing that have not yet received adequate research attention, by exploiting the data information of a program and provide a different testing coverage approach compared to existing control flow-oriented ones. The performance of the proposed approach is assessed and validated on a number of sample programs of different levels of size and complexity. The associated experimental results indicate successful performance in terms of testing coverage, which is significantly better when compared to those of existing dynamic data flow-oriented test data generation methods.