.png){kind=icon}
返回
A parallel framework for software defect detection and metric selection on cloud computing
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-017-0892-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the continued growth of Internet of Things (IoT) and its convergence with the cloud, numerous interoperable software are being developed for cloud. Therefore, there is a growing demand to maintain a better quality of software in the cloud for improved service. This is more crucial as the cloud environment is growing fast towards a hybrid model; a combination of public and private cloud model. Considering the high volume of the available software as a service (SaaS) in the cloud, identification of non-standard software and measuring their quality in the SaaS is an urgent issue. Manual testing and determination of the quality of the software is very expensive and impossible to accomplish it to some extent. An automated software defect detection model that is capable to measure the relative quality of software and identify their faulty components can significantly reduce both the software development effort and can improve the cloud service. In this paper, we propose a software defect detection model that can be used to identify faulty components in big software metric data. The novelty of our proposed approach is that it can identify significant metrics using a combination of different filters and wrapper techniques. One of the important contributions of the proposed approach is that we designed and evaluated a parallel framework of a hybrid software defect predictor in order to deal with big software metric data in a computationally efficient way for cloud environment. Two different hybrids have been developed using Fisher and Maximum Relevance (MR) filters with a Artificial Neural Network (ANN) based wrapper in the parallel framework. The evaluations are performed with real defect-prone software datasets for all parallel versions. Experimental results show that the proposed parallel hybrid framework achieves a significant computational speedup on a computer cluster with a higher defect prediction accuracy and smaller number of software metrics compared to the independent filter or wrapper approaches.
Keyword:
PREDICTION
DESIGN
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
C
IF:
4.1
论文数:
5.0K
被引数:
7.5K
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
.png){kind=icon}
PREAI
分享收藏A fuzzy logic based approach for phase-wise software defects prediction using software metrics基于模糊逻辑的基于软件度量的阶段软件缺陷预测方法
PREAI
分享收藏The Influence of Theory of Planned Behavior, Religion and Halal Certification on Consumers’ Purchase Intention of Halal Food at Restaurant in Subang Jaya计划行为理论、宗教信仰和清真认证对消费者在梳邦再也餐厅购买清真食品意愿的影响
PREAI
分享收藏
查看更多内容