A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION

A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION

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Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imb...

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Main Author: Saleh ALBAHLI
Format: Article
Language:English
Published: Polish Association for Knowledge Promotion 2020-09-01
Series:Applied Computer Science
Subjects:
deep neural network
unlabeled dataset
just-in-time defect prediction
unsupervised prediction
Online Access:http://acs.pollub.pl/pdf/v16n3/1.pdf
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spelling doaj-c4511f309b7c4e5599c1cdcb920154a02020-11-25T03:43:35ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772020-09-0116351510.23743/acs-2020-17A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTIONSaleh ALBAHLI0Qassim University, College of Computer, Department of Information Technology, Saudi Arabia, 51452, Qassim, salbahli@qu.edu.saSince the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.http://acs.pollub.pl/pdf/v16n3/1.pdfdeep neural networkunlabeled datasetjust-in-time defect predictionunsupervised prediction
collection DOAJ
language English
format Article
sources DOAJ
author Saleh ALBAHLI
spellingShingle Saleh ALBAHLI
A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
Applied Computer Science
deep neural network
unlabeled dataset
just-in-time defect prediction
unsupervised prediction
author_facet Saleh ALBAHLI
author_sort Saleh ALBAHLI
title A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
title_short A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
title_full A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
title_fullStr A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
title_full_unstemmed A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
title_sort deep ensemble learning method for effort-aware just-in-time defect prediction
publisher Polish Association for Knowledge Promotion
series Applied Computer Science
issn 1895-3735
2353-6977
publishDate 2020-09-01
description Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.
topic deep neural network
unlabeled dataset
just-in-time defect prediction
unsupervised prediction
url http://acs.pollub.pl/pdf/v16n3/1.pdf
work_keys_str_mv AT salehalbahli adeepensemblelearningmethodforeffortawarejustintimedefectprediction
AT salehalbahli deepensemblelearningmethodforeffortawarejustintimedefectprediction
_version_ 1724518896226009088

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