2015 IEEE International Conference on Software Quality, Reliability and Security最新文献

ASR: Abstraction Subspace Reduction for Exposing Atomicity Violation Bugs in Multithreaded Programs 多线程程序中原子性冲突错误的抽象子空间缩减

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.46

Shangru Wu, Chunbai Yang, W. Chan

{"title":"ASR: Abstraction Subspace Reduction for Exposing Atomicity Violation Bugs in Multithreaded Programs","authors":"Shangru Wu, Chunbai Yang, W. Chan","doi":"10.1109/QRS.2015.46","DOIUrl":"https://doi.org/10.1109/QRS.2015.46","url":null,"abstract":"Many two-phase based dynamic concurrency bug detectors predict suspicious instances of atomicity violation from one execution trace, and examine each such instance by scheduling a confirmation run. If the amount of suspicious instances predicted is large, confirming all these instances becomes a burden. In this paper, we present the first controlled experiment that evaluates the efficiency, effectiveness, and cost-effectiveness of reduction on suspicious instances in the detection of atomicity violations. A novel form of reduction technique named ASR is proposed. Our empirical analysis reveals many interesting findings: First, the reduced sets of instances produced by ASR significantly improve the efficiency of atomicity violation detection without significantly compromising the effectiveness. Second, ASR is significantly more cost-effective than random reduction and untreated reduction by 8.5 folds and 60.7 folds, respectively, in terms of mean normalized bug detection ratio. Third, six ASR techniques can be significantly more cost-effective than the technique modeled after a state-of-the-art detector.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"28 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":"115689833","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: 7

Combinatorial Testing for Tree-Structured Test Models with Constraints 具有约束的树结构测试模型的组合测试

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.29

Takashi Kitamura, A. Yamada, G. Hatayama, Cyrille Artho, Eun-Hye Choi, Thi Bich Ngoc Do, Y. Oiwa, Shin Sakuragi

引用次数: 12

Potential Component Leaks in Android Apps: An Investigation into a New Feature Set for Malware Detection Android应用程序中潜在的组件泄漏:对恶意软件检测新功能集的调查

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.36

Li Li, Kevin Allix, Daoyuan Li, Alexandre Bartel, Tegawendé F. Bissyandé, Jacques Klein

引用次数: 29

LLFI: An Intermediate Code-Level Fault Injection Tool for Hardware Faults 硬件故障的中间代码级故障注入工具

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.13

Qining Lu, Mostafa Farahani, Jiesheng Wei, Anna Thomas, K. Pattabiraman

引用次数: 73

Assessing Security to Compare Architecture Alternatives of Component-Based Systems 评估安全性以比较基于组件的系统的架构选择

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.24

A. Busch, M. Strittmatter, A. Koziolek

引用次数: 8

An Anomaly Detection System Based on Ensemble of Detectors with Effective Pruning Techniques 基于有效剪枝技术的检测器集成异常检测系统

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.25

Amirreza Soudi, Wael Khreich, A. Hamou-Lhadj

引用次数: 9

Fine-Grained Software Reliability Estimation Using Software Testing Inputs 使用软件测试输入的细粒度软件可靠性评估

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.22

H. Okamura, Yuki Takekoshi, T. Dohi

引用次数: 0

An Approach for Authenticating Smartphone Users Based on Histogram Features 一种基于直方图特征的智能手机用户认证方法

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.27

Chien-Cheng Lin, Chin-Chun Chang, Deron Liang

{"title":"An Approach for Authenticating Smartphone Users Based on Histogram Features","authors":"Chien-Cheng Lin, Chin-Chun Chang, Deron Liang","doi":"10.1109/QRS.2015.27","DOIUrl":"https://doi.org/10.1109/QRS.2015.27","url":null,"abstract":"In this study, we propose to adopt histogram features obtained from smartphone sensors for building authentication models, which could be used to nonintrusively authenticate smartphone users in varying operating scenarios (e.g. standing and sitting) when engaged in using stationary apps. We adopted two smartphone sensors, namely touchscreen and orientation sensor, to evaluate their feasibility. Consequently, sixteen touch-based features and thirty-three orientation-based features were separately used to construct two authentication models. To evaluate the performance of two constructed models, thirty-five subjects joined for collecting experimental data in two operating scenarios, standing and sitting. The experimental results showed that the equal error rate (EER) of touch-based model was approximately 6.56% with features extracted from ten flick touch gestures and reduced to approximately 3.05% with sixty flick touch gestures. For orientation-based model, the EERs were approximately 10.27% and 7.07%, separately. The results showed that the histogram features of the adopted two sensors are feasible for authentication purpose. Specially, this study further discusses the phenomenon of multiple behavioral pattern over the adopted two sensors caused among different operating scenarios, such as standing and sitting.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"20 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":"115311290","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: 1

Predicting Vulnerable Components via Text Mining or Software Metrics? An Effort-Aware Perspective 通过文本挖掘还是软件度量来预测易受攻击的组件?努力意识的视角

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.15

Yaming Tang, Fei Zhao, Yibiao Yang, Hongmin Lu, Yuming Zhou, Baowen Xu

{"title":"Predicting Vulnerable Components via Text Mining or Software Metrics? An Effort-Aware Perspective","authors":"Yaming Tang, Fei Zhao, Yibiao Yang, Hongmin Lu, Yuming Zhou, Baowen Xu","doi":"10.1109/QRS.2015.15","DOIUrl":"https://doi.org/10.1109/QRS.2015.15","url":null,"abstract":"In order to identify vulnerable software components, developers can take software metrics as predictors or use text mining techniques to build vulnerability prediction models. A recent study reported that text mining based models have higher recall than software metrics based models. However, this conclusion was drawn without considering the sizes of individual components which affects the code inspection effort to determine whether a component is vulnerable. In this paper, we investigate the predictive power of these two kinds of prediction models in the context of effort-aware vulnerability prediction. To this end, we use the same data sets, containing 223 vulnerabilities found in three web applications, to build vulnerability prediction models. The experimental results show that: (1) in the context of effort-aware ranking scenario, text mining based models only slightly outperform software metrics based models, (2) in the context of effort-aware classification scenario, text mining based models perform similarly to software metrics based models in most cases, and (3) most of the effect sizes (i.e. the magnitude of the differences) between these two kinds of models are trivial. These results suggest that, from the viewpoint of practical application, software metrics based models are comparable to text mining based models. Therefore, for developers, software metrics based models are practical choices for vulnerability prediction, as the cost to build and apply these models is much lower.","PeriodicalId":361839,"journal":{"name":"2015 IEEE International Conference on Software Quality, Reliability and Security","volume":"248 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":"133800785","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: 29

Machine Learning Based Hybrid Behavior Models for Android Malware Analysis 基于机器学习的Android恶意软件混合行为模型分析

2015 IEEE International Conference on Software Quality, Reliability and Security Pub Date : 2015-08-03 DOI: 10.1109/QRS.2015.37

Hsin-Yu Chuang, Sheng-De Wang

引用次数: 41