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Software fault prediction with imbalanced datasets using SMOTE-Tomek sampling technique and Genetic Algorithm models

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Abstract

Over the years, there has been a considerable discussion regarding machine learning (ML) techniques to forecast software faults. It can be challenging to choose a suitable machine learning technique for fault prediction modelling because of variations in the prediction performance of machine learning techniques for software systems. The evaluation of previously presented software fault prediction (SFP) approaches revealed that single machine learning-based models that did not deliver the best accuracy and F1-Score in any context, emphasizing the need to use multiple techniques, such as sampling and selection, in addition to the application of machine learning models. In order to address this issue, we present and discuss a method for predicting software faults that rely on choosing the most suitable machine learning and deep learning techniques from a pool of accurate and competitive learning techniques in order to construct a fault prediction model. The presented approach chooses the best features using Mutual Information feature selection technique. Using a hybrid sampling (SMOTE-Tomek) techniques, the issue of class imbalance (CI) is addressed. Then finally a Genetic Algorithm based machine learning (GA-DT) and a deep learning model (ANN-DT) are developed for the purpose for predicting faults in a software. For empirical evaluation, Eclipse version dataset (2.0, 2.1, and 3.0) is considered. Precision, recall, accuracy, and f1-Score are the performance metrics we used to evaluate the effectiveness of the proposed approach. The results demonstrated that the proposed approach (GA-DT and ANN-DT) effectively predicted the software's faults with ANN-DT providing best accuracies for all the three versions of Eclipse dataset.

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Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. We have use publicly available Eclipse version dataset (Eclipse 2.0, Eclipse 2.1, and Eclipse 3.0).

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  1. Birla Institute of Technology, Ranchi, Mesra, India

    Mansi Gupta, Kumar Rajnish & Vandana Bhattacharjee

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  1. Mansi Gupta
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Gupta, M., Rajnish, K. & Bhattacharjee, V. Software fault prediction with imbalanced datasets using SMOTE-Tomek sampling technique and Genetic Algorithm models. Multimed Tools Appl 83, 47627–47648 (2024). https://doi.org/10.1007/s11042-023-16788-7

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