A Genetic Algorithm-Based Stress Test Requirements Generator Tool and Its Empirical Evaluation

Genetic algorithms (GAs) have been applied previously to UML-driven, stress test requirements generation with the aim of increasing chances of discovering faults relating to network traffic in distributed real-time systems. However, since evolutionary algorithms are heuristic, their performance can vary across multiple executions, which may affect robustness and scalability. To address this, we present the design and technical detail of a UML-driven, GA-based stress test requirements generation tool, together with its empirical analysis. The main goal is to analyze and improve the applicability, efficiency and effectiveness and also to validate the design choices of the GA used in the tool. Findings of the empirical evaluation reveal that the tool is robust and reasonably scalable when it is executed on large-scale experimental design models. The study also reveals the main bottlenecks and limitations of the tools, e.g., there is a performance bottleneck when the system under test has ...

• Type: Journal
• Year: 2010
• Where: TSE
• Authors: Vahid Garousi
• Keywords: Artificial Intelligence, Stress Test Requirements, Test Requirements, Tools, TSE 2010