We propose a multi-objective genetic algorithm method to prioritize state-based test cases to achieve several competing objectives such as budget and coverage of data flow information, while hopefully detecting faults as early as possible when executing prioritized test cases. The experimental results indicate that our approach is useful and effective: prioritizations quickly achieve maximum data flow coverage and this results in early fault detection; prioritizations perform much better than random orders with much smaller variance.

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Keywords Genetic algorithm, Multi-objective optimization, Prioritization, State-based testing
Persistent URL dx.doi.org/10.1007/978-3-642-39742-4_7
Briand, L. (Lionel), Labiche, Y, & Chen, K. (Kathy). (2013). A multi-objective genetic algorithm to rank state-based test cases. doi:10.1007/978-3-642-39742-4_7