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Reliability modeling using Particle Swarm Optimization

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Abstract

Software quality includes many attributes including reliability of a software. Prediction of reliability of a software in early phases of software development will enable software practitioners in developing robust and fault tolerant systems. The purpose of this paper is to predict software reliability, by estimating the parameters of Software Reliability Growth Models (SRGMs). SRGMs are the mathematical models which generally reflect the properties of the process of fault detection during testing. Particle Swarm Optimization (PSO) has been applied to several optimization problems and has showed good performance. PSO is a popular machine learning algorithm under the category of Swarm Intelligence. PSO is an evolutionary algorithm like Genetic Algorithm (GA). In this paper we propose the use of PSO algorithm to the SRGM parameter estimation problem, and then compare the results with those of GA. The results are validated using data obtained from 16 projects. The results obtained from PSO have high predictive ability which is reflected by low error predictions. The results obtained using PSO are better than those obtained from GA. Hence, PSO may be used to estimate SRGM parameters.

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Abbreviations

PSO:

Particle Swarm Optimization

GA:

Genetic algorithm

SRGM:

Software reliability growth model

NHPP:

Non homogeneous poisson process

SI:

Swarm intelligence

SS:

Swarm size

NN:

Neural network

FF:

Fitness function

RMSE:

Root mean square error

MARE:

Mean absolute relative error

MRE:

Mean relative error

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Correspondence to Ruchika Malhotra.

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Malhotra, R., Negi, A. Reliability modeling using Particle Swarm Optimization. Int J Syst Assur Eng Manag 4, 275–283 (2013). https://doi.org/10.1007/s13198-012-0139-0

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  • DOI: https://doi.org/10.1007/s13198-012-0139-0

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