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An Efficient Method for Software Reliability Growth Model Selection Using Modified Particle Swarm Optimization Technique

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Software reliability engineering has become one of the established trends of research areas and practices in software engineering that dealt with the measurement and enhancement of reliability of any software. This paper proposes an efficient software reliability growth model (SRGM) model selection for estimating the reliability of the software. The selected model should offer increased failure rate recognition and should resolve the faults in software with better quality estimation. For the selection of a suitable model we have used optimization technique; as a result, an optimized model for reliability estimation is obtained. The optimization technique we employ in our proposed method is particle swarm optimization where an improvement is carried out in selecting the fitness by incorporating genetic operators like crossover and mutation while updating the velocity and position. The Modified Particle Swarm Optimization (MPSO) employed in our proposed method delivers better-optimized result in selecting the proper reliability growth model for the parameter estimations. Once the specific reliability growth model chosen from different available models, the reliability of the software estimated along with various parameters like the cumulative number of failure and failure rate. The proposed method implemented on the working platform of JAVA and the effectiveness of the proposed method estimated by comparing with existing methods.
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Software Reliability Growth Model; Modified Particle Swarm Optimization; Two-Dimensional S-shaped Model; Huang Logistic Model; Yamada-Rayleigh Model

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