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

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ID: 88103
2019
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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.
Reference Key
albahli2019afuture Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Albahli, Saleh;
Journal future internet
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/1999-5903/11/12/246
Keywords
Medicine Information technology ophthalmology history of scholarship and learning. the humanities

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