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Heterogeneous stacked ensemble classifier for software defect prediction

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Abstract

Software defect prediction (SDP) plays an important role to ensure that software meets quality standards; by highlighting the modules which are prone to errors and hence allows to focus the test efforts on them. Class imbalance nature of the defect dataset hinders the defect predictors to correctly classify the buggy modules. Here, we introduce a novel heterogenous ensemble classifier built with stacking methodology to overcome this problem of imbalanced datasets and hence, significant improvement in the prediction power is being proposed. Stacked ensemble is achieved with the best known classifiers from SDP literature as base classifiers (artificial neural network, nearest neighbor, tree based classifier, Bayesian classifier and support vector machines). For experimental work, five public datasets from NASA corpus are used. A comparative analysis for the proposed heterogenous stacking based ensemble method is made with the base classifiers and with the state-of-the art ensemble based SDP models over the evaluation criteria of ROC, AUC and accuracy. It is found that the proposed heterogenous stacking based ensemble classifier outperforms the base classifiers by 12% in terms of AUC score and by 8% in terms of Accuracy. It improves the performance of state-of-the-art ensemble methods by 4% in terms of AUC score and by 9% in terms of Accuracy. It can be concluded from the comparative analysis that the proposed SDP classifier is best performer among the candidate SDP classifiers statistically.

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Authors and Affiliations

  1. Manipal University Jaipur, Jaipur, Rajasthan, 303007, India

    Somya Goyal

  2. Guru Jambheshwar University of Science & Technology, Hisar, Haryana, 125001, India

    Somya Goyal & Pradeep Kumar Bhatia

Authors
  1. Somya Goyal
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  2. Pradeep Kumar Bhatia
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Goyal, S., Bhatia, P.K. Heterogeneous stacked ensemble classifier for software defect prediction. Multimed Tools Appl 81, 37033–37055 (2022). https://doi.org/10.1007/s11042-021-11488-6

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