2015 IEEE International Conference on Software Quality, Reliability and Security最新文献_第4页

Node-Set Analysis for Linked Recursive Data Structures 链接递归数据结构的节点集分析

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.19

Zhenhao Tang, Hanfei Wang, Bin Li, Juan Zhai, Jianhua Zhao, Xuandong Li

引用次数: 0

Cross-Project Aging Related Bug Prediction 跨项目老化相关的Bug预测

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.17

Fangyun Qin, Zheng Zheng, Chenggang Bai, Yu Qiao, Zhenyu Zhang, Cheng Chen

{"title":"Cross-Project Aging Related Bug Prediction","authors":"Fangyun Qin, Zheng Zheng, Chenggang Bai, Yu Qiao, Zhenyu Zhang, Cheng Chen","doi":"10.1109/QRS.2015.17","DOIUrl":"https://doi.org/10.1109/QRS.2015.17","url":null,"abstract":"In a long running system, software tends to encounter performance degradation and increasing failure rate during execution, which is called software aging. The bugs contributing to the phenomenon of software aging are defined as Aging Related Bugs (ARBs). Lots of manpower and economic costs will be saved if ARBs can be found in the testing phase. However, due to the low presence probability and reproducing difficulty of ARBs, it is usually hard to predict ARBs within a project. In this paper, we study whether and how ARBs can be located through cross-project prediction. We propose a transfer learning based aging related bug prediction approach (TLAP), which takes advantage of transfer learning to reduce the distribution difference between training sets and testing sets while preserving their data variance. Furthermore, in order to mitigate the severe class imbalance, class imbalance learning is conducted on the transferred latent space. Finally, we employ machine learning methods to handle the bug prediction tasks. The effectiveness of our approach is validated and evaluated by experiments on two real software systems. It indicates that after the processing of TLAP, the performance of ARB bug prediction can be dramatically improved.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128288006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

An Effective Approach to Continuous User Authentication for Touch Screen Smart Devices 触摸屏智能设备持续用户认证的有效方法

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.40

Arun Balaji Buduru, S. Yau

{"title":"An Effective Approach to Continuous User Authentication for Touch Screen Smart Devices","authors":"Arun Balaji Buduru, S. Yau","doi":"10.1109/QRS.2015.40","DOIUrl":"https://doi.org/10.1109/QRS.2015.40","url":null,"abstract":"Due to the rapid increase in the use of personal smart devices, more sensitive data is stored and viewed on these smart devices. This trend makes it easier for attackers to access confidential data by physically compromising (including stealing) these smart devices. Currently, most personal smart devices employ one of the one-time user authentication schemes, such as four-to-six digits, fingerprint or pattern-based schemes. These authentication schemes are often not good enough for securing personal smart devices because the attackers can easily extract all the confidential data from the smart device by breaking such schemes, or by keeping the authenticated session open on a physically compromised smart device. In addition, existing re-authentication or continuous authentication techniques for protecting personal smart devices use centralized architecture and require servers at a centralized location to train and update the learning model used for continuous authentication, which impose additional communication overhead. In this paper, an approach is presented to generating and updating the authentication model on the user's smart device with user's gestures, instead of a centralized server. There are two major advantages in this approach. One is that this approach continuously learns and authenticates finger gestures of the user in the background without requiring the user to provide specific gesture inputs. The other major advantage is to have better authentication accuracy by treating uninterrupted user finger gestures over a short time interval as a single gesture for continuous user authentication.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130117460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

An Empirical Study of Dynamic Incomplete-Case Nearest Neighbor Imputation in Software Quality Data 软件质量数据中动态不完全案例最近邻法的实证研究

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.16

Jianglin Huang, Hongyi Sun, Yan-Fu Li, Min Xie

引用次数: 4

Hovac: A Configurable Fault Injection Framework for Benchmarking the Dependability of C/C++ Applications Hovac:一个可配置的故障注入框架，用于测试C/ c++应用程序的可靠性

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.12

Lena Feinbube, D. Richter, A. Polze

引用次数: 6

A Case Study on Code Generation of an ERP System from Event-B 基于Event-B的ERP系统代码生成案例研究

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.34

Néstor Cataño, Tim Wahls

引用次数: 13

PORA: Proportion-Oriented Randomized Algorithm for Test Case Prioritization 面向比例的测试用例优先排序随机算法

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.28

Bo Jiang, W. Chan, T. Tse

引用次数: 8

Deep Learning for Just-in-Time Defect Prediction 实时缺陷预测的深度学习

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-01 DOI: 10.1109/QRS.2015.14

Xinli Yang, D. Lo, Xin Xia, Yun Zhang, Jianling Sun

{"title":"Deep Learning for Just-in-Time Defect Prediction","authors":"Xinli Yang, D. Lo, Xin Xia, Yun Zhang, Jianling Sun","doi":"10.1109/QRS.2015.14","DOIUrl":"https://doi.org/10.1109/QRS.2015.14","url":null,"abstract":"Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of expressive features from a set of initial change features by leveraging a deep belief network algorithm. Next, a machine learning classifier is built on the selected features. To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average). In addition, Deeper can achieve F1-scores of 0.22-0.63, which are statistically significantly higher than those of Kamei et al.'s approach on 4 out of the 6 projects.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 283