Empirical analysis of a genetic algorithm-based stress test technique

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Empirical analysis of a genetic algorithm-based stress test technique
Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to support various test automation tasks. Since evolutionary algorithms are heuristics, their performance and output efficiency can vary across multiple runs. Therefore, there is a strong need to empirically investigate the capacity of evolutionary test techniques to achieve the desired objectives (e.g., generate stress test cases) and their scalability in terms of the complexity of the System Under Test (SUT), the inputs, and the control parameters of the search algorithms. In a previous work, we presented a GA-based UML-driven, stress test technique aimed at increasing chances of discovering faults related to network traffic in distributed real-time software. This paper reports a carefully-designed empirical study which was conducted to analyze and improve the applicability, efficiency and effectiveness of the above GA-based stress test technique. Detailed stages and objectives of the em...
Vahid Garousi
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Authors Vahid Garousi
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