Ensemble-based software fault prediction with two staged data pre-processing
by Shubham P. Kulkarni; Sanjeev Patel
International Journal of Computer Applications in Technology (IJCAT), Vol. 72, No. 3, 2023

Abstract: Software fault prediction is the process of identifying the software modules which are more likely to be defective or faulty before the testing phase of software development life-cycle model. We use software metric values of different modules for the known software project to train the software fault prediction model. Our objective is to implement the ensemble-based models on software fault data sets along with feature selection and data re-sampling techniques to achieve the improved performance. In this paper, we have designed a two-stage data pre-processing technique on the data set before passing it through the ensemble-based model for training. It has been found that the two-stage pre-processing model outperforms the general ensemble-based model. It gives an improvement of 1 to 6% for all the used classifiers viz., Bagging, Dagging, Rotation Forest, Random Forest and AdaBoost.

Online publication date: Mon, 11-Sep-2023

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