A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Saleh Albahli
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Saleh Albahli: Department of Information Technology, College of Computer, Qassim University, Buraidah 51452, Saudi Arabia

Future Internet, 2019, vol. 11, issue 12, 1-13

Abstract: Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.

Keywords: deep neural network; unlabeled dataset; just-in-time defect prediction; unsupervised prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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