Empirical analysis of a genetic algorithm-based stress test technique

Vahid Garousi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

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 languageEnglish
Title of host publicationGECCO'08
Subtitle of host publicationProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Pages1743-1750
Number of pages8
Publication statusPublished - 15 Dec 2008
Externally publishedYes
Event10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States
Duration: 12 Jul 200816 Jul 2008

Conference

Conference10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
CountryUnited States
CityAtlanta, GA
Period12/07/200816/07/2008

Keywords

  • Empirical analysis
  • Genetic algorithms
  • Stress testing

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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