Deep Learning Software Defect Prediction Methods for Cloud Environments Research

This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of dif...

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Autores principales:	Wenjian Liu, Baoping Wang, Wennan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/bac731d7800a46d999fe26cd99bca75b
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spelling	oai:doaj.org-article:bac731d7800a46d999fe26cd99bca75b2021-11-29T00:57:04ZDeep Learning Software Defect Prediction Methods for Cloud Environments Research1875-919X10.1155/2021/2323100https://doaj.org/article/bac731d7800a46d999fe26cd99bca75b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2323100https://doaj.org/toc/1875-919XThis paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.Wenjian LiuBaoping WangWennan WangHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Wenjian Liu Baoping Wang Wennan Wang Deep Learning Software Defect Prediction Methods for Cloud Environments Research
description	This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.
format	article
author	Wenjian Liu Baoping Wang Wennan Wang
author_facet	Wenjian Liu Baoping Wang Wennan Wang
author_sort	Wenjian Liu
title	Deep Learning Software Defect Prediction Methods for Cloud Environments Research
title_short	Deep Learning Software Defect Prediction Methods for Cloud Environments Research
title_full	Deep Learning Software Defect Prediction Methods for Cloud Environments Research
title_fullStr	Deep Learning Software Defect Prediction Methods for Cloud Environments Research
title_full_unstemmed	Deep Learning Software Defect Prediction Methods for Cloud Environments Research
title_sort	deep learning software defect prediction methods for cloud environments research
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/bac731d7800a46d999fe26cd99bca75b
work_keys_str_mv	AT wenjianliu deeplearningsoftwaredefectpredictionmethodsforcloudenvironmentsresearch AT baopingwang deeplearningsoftwaredefectpredictionmethodsforcloudenvironmentsresearch AT wennanwang deeplearningsoftwaredefectpredictionmethodsforcloudenvironmentsresearch
_version_	1718407669350400000

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

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