Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Art of Software Testing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Applying particle swarm optimization to software testing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage
APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
Automatic, evolutionary test data generation for dynamic software testing
Journal of Systems and Software
Handling Constraints for Search Based Software Test Data Generation
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
Tabu search-based metaheuristic algorithm for software system reliability problems
Computers and Operations Research
Evolutionary White-Box Software Test with the EvoTest Framework: A Progress Report
ICSTW '09 Proceedings of the IEEE International Conference on Software Testing, Verification, and Validation Workshops
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
Hi-index | 0.00 |
Various studies on generating test data have been done up to date, but few test data generated by these studies can effectively detect faults lying in the program. We focus on the problem of generating test data for both paths coverage and faults detection. First, the problem above is formulated as a bi-objective optimization problem with one constraint, whose two objectives are the number of faults detected in the traversed path and the risk level of these faults, respectively, and the unique constraint is that the traversed path is just the target one; then, a multi-objective evolutionary algorithm is employed to effectively solve the formulated model; finally, the proposed method is applied in bubble sort program manually injected with some faults, and compared with the random method and the evolutionary optimization one without the task of detecting faults. The experimental results confirm the advantage of our method.