Title: Regression-based software fault prediction using biogeography-based optimisation (R-BBO)

Authors: Aarti; Geeta Sikka; Renu Dhir

Addresses: Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, India ' Department of Computer Science and Engineering, NIT, Jalandhar, India ' Department of Computer Science and Engineering, NIT, Jalandhar, India

Abstract: It is difficult to build model of accurate estimate due to the inherent uncertainty and similarity among different categories in development projects. In this paper, fault prediction is done using biogeography-based optimisation (BBO) with the goal of recognising the faults in software systems in more efficient way. Our methodology includes four steps as follows: 1) firstly pre-processing was employed to remove redundant data; 2) secondly, relevant features are extracted using principal component analysis; 3) thirdly, fault-prediction system based on the optimisation of regression parameter using biogeography-based optimisation (R-BBO) was proposed. The experiment employed over different fault related datasets using ten-fold cross validation. The results showed that proposed prediction system (R-BBO) yield an overall accuracy of 85.4% (predicted over five datasets) which is higher than the prediction using genetic algorithm (R-GA). The proposed R-BBO was effective in terms of classification accuracy, precision and recall.

Keywords: fault-classification; genetic algorithm; regression; biogeography-based optimisation; BBO.

DOI: 10.1504/IJISDC.2019.105803

International Journal of Intelligent Systems Design and Computing, 2019 Vol.3 No.1, pp.49 - 63

Accepted: 14 Nov 2019
Published online: 05 Mar 2020 *

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