Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Generating Test Data for Both Paths Coverage and Faults Detection Using Genetic Algorithms
verfasst von : Dun-wei Gong, Yan Zhang
Erschienen in: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.