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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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