Abstract
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 empirical analysis are reported. The findings of the study are furthermore used to better calibrate the parameters of the GA-based stress test technique.
Original language | English |
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Title of host publication | GECCO'08 |
Subtitle of host publication | Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 |
Pages | 1743-1750 |
Number of pages | 8 |
Publication status | Published - 15 Dec 2008 |
Externally published | Yes |
Event | 10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States Duration: 12 Jul 2008 → 16 Jul 2008 |
Conference
Conference | 10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 12/07/2008 → 16/07/2008 |
Keywords
- Empirical analysis
- Genetic algorithms
- Stress testing
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Software