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Improved prediction of software defects using ensemble machine learning techniques

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Abstract

Software testing process is a crucial part in software development. Generally the errors made by developers get fixed at a later stage of the software development process. This increases the impact of the defect. To prevent this, defects need to be predicted during the initial days of the software development, which in turn helps in efficient utilization of the testing resources. Defect prediction process involves classification of software modules into defect prone and non-defect prone. This paper aims to reduce the impact of two major issues faced during defect prediction, i.e., data imbalance and high dimensionality of the defect datasets. In this research work, various software metrics are evaluated using feature selection techniques such as Recursive Feature Elimination (RFE), Correlation-based feature selection, Lasso, Ridge, ElasticNet and Boruta. Logistic Regression, Decision Trees, K-nearest neighbor, Support Vector Machines and Ensemble Learning are some of the algorithms in machine learning that have been used in combination with the feature extraction and feature selection techniques for classifying the modules in software as defect prone and non-defect prone. The proposed model uses combination of Partial Least Square (PLS) Regression and RFE for dimension reduction which is further combined with Synthetic Minority Oversampling Technique due to the imbalanced nature of the used datasets. It has been observed that XGBoost and Stacking Ensemble technique gave best results for all the datasets with defect prediction accuracy more than 0.9 as compared to algorithms used in the research work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835315, India

    Sweta Mehta & K. Sridhar Patnaik

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  1. Sweta Mehta
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  2. K. Sridhar Patnaik
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Correspondence to Sweta Mehta.

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Mehta, S., Patnaik, K.S. Improved prediction of software defects using ensemble machine learning techniques. Neural Comput & Applic 33, 10551–10562 (2021). https://doi.org/10.1007/s00521-021-05811-3

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