Abstract
The quality of Software comprises many features constituting of software reliability. Estimating of software reliability in the initial stage of platform establishment will allow a software professional in originating tables well as defect long-suffering software. Testing and maintaining Software is terribly exorbitant and strenuous, and it has been predicted that about half of software establishment expenses are designated to validating of the software. In view of this we propose nature inspires methods of Particle Swarm Optimization (PSO) based model to predict software failure. The proposed model is compared with some existing benchmark techniques like Neural Networks (NN), Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, and genetic algorithm (GA). The dataset considered for experiments are taken from NASA Promise Software Engineering Repository projects. The prediction generated by PSO is more accurate as compared with other benchmark techniques.
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Habtemariam, G.M., Mohapatra, S.K., Seid, H.W. (2022). Prediction of Software Reliability Using Particle Swarm Optimization. In: Panda, M., et al. Innovations in Intelligent Computing and Communication. ICIICC 2022. Communications in Computer and Information Science, vol 1737. Springer, Cham. https://doi.org/10.1007/978-3-031-23233-6_11
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DOI: https://doi.org/10.1007/978-3-031-23233-6_11
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