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영어
This paper presents a technique that based on a combination of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called GPSCA (Genetic-Particle Swarm Combined Algorithm) which is used to generate automatic test data for data flow coverage with using dominance concept between two nodes. The performance of the proposed approach is analyzed on a number of programs having different size and complexity. Finally, the performance of GPSCA is compared to both GA and PSO for generation of automatic test cases to demonstrate its superiority.
목차
Abstract
1. Introduction
2. Background
2.1. The Control Flow Graph
2.2. Dominance Tree
2.3. Data Flow Analysis Technique
3. GA and PSO
3.1. Genetic Algorithms
3.2. Particle Swarm Optimization (PSO)
3.3. GPSCA
4. Fitness Function
5. Experimental Results
6. Conclusion and Future Work
References
1. Introduction
2. Background
2.1. The Control Flow Graph
2.2. Dominance Tree
2.3. Data Flow Analysis Technique
3. GA and PSO
3.1. Genetic Algorithms
3.2. Particle Swarm Optimization (PSO)
3.3. GPSCA
4. Fitness Function
5. Experimental Results
6. Conclusion and Future Work
References
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자료제공 : 네이버학술정보