Regression-based software fault prediction using biogeography-based optimisation (R-BBO)
by Aarti; Geeta Sikka; Renu Dhir
International Journal of Intelligent Systems Design and Computing (IJISDC), Vol. 3, No. 1, 2019

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.

Online publication date: Fri, 13-Mar-2020

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