.png){kind=icon}
返回
LDFR: Learning deep feature representation for software defect prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2019.110402.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Software Defect Prediction (SDP) aims to detect defective modules to enable the reasonable allocation of testing resources, which is an economically critical activity in software quality assurance. Learning effective feature representation and addressing class imbalance are two main challenges in SDP. Ideally, the more discriminative the features learned from the modules and the better the rescue performed on the imbalance issue, the more effective it should be in detecting defective modules. In this study, to solve these two challenges, we propose a novel framework named LDFR by Learning Deep Feature Representation from the defect data for SDP. Specifically, we use a deep neural network with a new hybrid loss function that consists of a triplet loss to learn a more discriminative feature representation of the defect data and a weighted cross-entropy loss to remedy the imbalance issue. To evaluate the effectiveness of the proposed LDFR framework, we conduct extensive experiments on a benchmark dataset with 27 defect data (each with three types of features), using three traditional and three effort-aware indicators. Overall, the experimental results demonstrate the superiority of our LDFR framework in detecting defective modules when compared with 27 baseline methods, except in terms of the indicator of Precision. (C) 2019 Published by Elsevier Inc.
Keyword:
Software defect prediction
Deep feature representation
Triplet loss
Weighted cross-entropy loss
Deep neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
5.5K
被引数:
8.4K
机构
引用论文
Cost-sensitive boosting neural networks for software defect prediction基于代价敏感boosting神经网络的软件缺陷预测
PREAI
分享收藏
分享收藏分享收藏
Acute neurological deterioration in ovarioleukodystrophy related to EIF2B mutations: pregnancy with oocyte donation is a potentially precipitating factor与EIF2B突变相关的卵巢脑白质营养不良的急性神经系统恶化: 卵母细胞捐赠的妊娠是潜在的诱发因素
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容