Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
Generating test data for both paths coverage and faults detection using genetic algorithms
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
ACM SIGSOFT Software Engineering Notes
Search based software test data generation for structural testing: a perspective
ACM SIGSOFT Software Engineering Notes
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
Automatic generation of basis test paths using variable length genetic algorithm
Information Processing Letters
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This paper presents an automatic test-data generation technique that uses a genetic algorithm (GA) to generate test data that satisfy data-flow coverage criteria. The technique applies the concepts of dominance relations between nodes to define a new multi-objective fitness function to evaluate the generated test data. The paper also presents the results of a set of empirical studies conducted on a set of programs that evaluate the effectiveness of our technique compared to the random-testing technique. The studies show the effective of our technique in achieving coverage of the test requirements, and in reducing the size of test suites, the search time, and the number of iterations required to satisfy the data-flow criteria.