2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)最新文献_第6页

A Survey of the Use of Test Report in Crowdsourced Testing 众包测试中测试报告使用情况调查

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00062

Song Huang, Hao Chen, Zhan-wei Hui, Yuchan Liu

{"title":"A Survey of the Use of Test Report in Crowdsourced Testing","authors":"Song Huang, Hao Chen, Zhan-wei Hui, Yuchan Liu","doi":"10.1109/QRS51102.2020.00062","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00062","url":null,"abstract":"With the rise of crowdsourced software testing in recent years, the issuers of crowd test tasks can usually collect a large number of test reports after the end of the task. These reports have insufficient validity and completeness, and manual review often takes a lot of time and effort. The crowdsourced test task publisher hopes that after the crowdsourced platform collects the test report, it can analyze the validity and completeness of the report to determine the severity of the report and improve the efficiency of crowdsourced software testing. In the past ten years, researchers have used various technologies (such as natural language processing, information retrieval, machine learning, deep learning) to assist in analyzing reports to improve the efficiency of report review. We have summarized the relevant literature of report analysis in the past ten years, and then classified from report classification, duplicate report detection, report prioritization, report refactoring, and summarized the most important research work in each area. Finally, we propose research trends in these areas and analyze the challenges and opportunities facing crowdsourced test report analysis.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084448","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

Have It Your Way: Generating Customized Log Data Sets with a Model-driven Simulation Testbed 用你的方式:用模型驱动的仿真试验台生成定制的日志数据集

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00019

Max Landauer, Florian Skopik, Markus Wurzenberger, Wolfgang Hotwagner, A. Rauber

{"title":"Have It Your Way: Generating Customized Log Data Sets with a Model-driven Simulation Testbed","authors":"Max Landauer, Florian Skopik, Markus Wurzenberger, Wolfgang Hotwagner, A. Rauber","doi":"10.1109/QRS51102.2020.00019","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00019","url":null,"abstract":"Evaluations of intrusion detection systems (IDS) require log data sets collected in realistic system environments. Ex-isting testbeds therefore offer user simulations and attack scenarios that target specific use-cases. However, not only does the preparation of such testbeds require domain knowledge and time-consuming work, but also maintenance and modifications for other use-cases involve high manual efforts and repeated execution of tasks. We therefore propose to generate testbeds for IDS evaluation using strategies from model-driven engineering. In particular, our approach models system infrastructure, simulated normal behavior, and attack scenarios as testbed-independent modules. A transformation engine then automatically generates arbitrary numbers of testbeds, each with a particular set of characteristics and capable of running in parallel. Our approach greatly improves configurability and flexibility of testbeds and allows to reuse components across multiple scenarios. We use our proof-of-concept implementation to generate a labeled data set for IDS evaluation that is published with this paper.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130869956","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: 11

An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection 一种集网络级入侵检测和主机级入侵检测于一体的入侵检测系统

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00028

Jian K. Liu, Kun Xiao, Lei Luo, Yun Li, Lirong Chen

引用次数: 11

PHM Technology for Memory Anomalies in Cloud Computing for IaaS 面向IaaS的云计算中内存异常的PHM技术

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00018

Xiwei Qiu, Yuan-Shun Dai, Peng Sun, Xin Jin

{"title":"PHM Technology for Memory Anomalies in Cloud Computing for IaaS","authors":"Xiwei Qiu, Yuan-Shun Dai, Peng Sun, Xin Jin","doi":"10.1109/QRS51102.2020.00018","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00018","url":null,"abstract":"The IaaS (Infrastructure as a Service) is one of the most popular services from todays cloud service providers, where the virtual machines (VM) are rented by users who can deploy any program they want in the VMs to make their own websites or use as their remote desktops. However, this poses a major challenge for cloud IaaS providers who cannot control the software programs that users develop, install or download on their rented VMs. Those programs may not be well developed with various bugs or even downloaded/installed together with virus, which often make damages to the VMs or infect the cloud platform. To keep the health of a cloud IaaS platform, it is very important to implement the PHM (Prognostics and Health Management) technology for detecting those software problems and self-healing them in an intelligent and timely way. This paper realized a novel PHM technology inspired by biological autonomic nervous system to deal with the memory anomalies of those programs running on the cloud IaaS platform. We first present an innovative autonomic computing technology called Bionic Autonomic Nervous System (BANS) to endow the cloud system with distinctive capabilities of perception, detection, reflection, and learning. Then, we propose a BANS-based Prognostics and Health Management (BPHM) technology to enable the cloud system self-dealing with various memory anomalies. AI-based failure prognostics, immediate self-healing, self-learning ability and self-improvement functions are implemented. Experimental results illustrate that the designed BPHM can automatically and intelligently deal with complex memory anomalies in a real cloud system for IaaS, to keep the system much more reliable and healthier.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134558871","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: 3

A Multi-Objective Learning Method for Building Sparse Defect Prediction Models 稀疏缺陷预测模型的多目标学习方法

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00037

Xin Li, Xiaoxing Yang, Jianmin Su, Wushao Wen

引用次数: 1

Incorporating Android Code Smells into Java Static Code Metrics for Security Risk Prediction of Android Applications 将Android代码气味整合到Java静态代码度量中用于Android应用程序的安全风险预测

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00017

Ai Gong, Yi Zhong, W. Zou, Yangyang Shi, Chunrong Fang

{"title":"Incorporating Android Code Smells into Java Static Code Metrics for Security Risk Prediction of Android Applications","authors":"Ai Gong, Yi Zhong, W. Zou, Yangyang Shi, Chunrong Fang","doi":"10.1109/QRS51102.2020.00017","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00017","url":null,"abstract":"With the wide-spread use of Android applications in people’s daily life, it becomes more and more important to timely identify the security problems of these applications. To enrich existing studies in guarding the security and privacy of Android applications, we attempted to predict the security risk levels of Android applications. Specifically, we proposed an approach that incorporated Android code smells into traditional Java code metrics to predict how secure an Android application is. With an evaluation of our technique on 3,680 Android applications, we found that: (1) Android code smells could help improve the performance of security risk prediction of Android applications; (2) By building a Random Forest model based on Android code smells and Java code metrics, we could achieve an Area Under Curve (AUC) of 0.97; (3) Android code smells such as member ignoring method (MIM) and leaking inner class (LIC) have a relatively-large influence on Android security risk prediction, to which developers should pay more attention during their application development.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121480666","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: 4

An Approach to Determine the Optimal k-Value of K-means Clustering in Adaptive Random Testing 自适应随机检验中k-均值聚类最优k值的确定方法

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00032

Jinfu Chen, Lingling Zhao, Minmin Zhou, Yisong Liu, Songling Qin

{"title":"An Approach to Determine the Optimal k-Value of K-means Clustering in Adaptive Random Testing","authors":"Jinfu Chen, Lingling Zhao, Minmin Zhou, Yisong Liu, Songling Qin","doi":"10.1109/QRS51102.2020.00032","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00032","url":null,"abstract":"Adaptive Random Testing (ART) aims at improving detection effectiveness by evenly distributing test cases over the whole input domain. Many ART algorithms introducing clustering techniques (such as k-means Clustering) have been proposed to achieve an even spread of test cases. Though previous studies have demonstrated that ART with k-means clustering could achieve a good enhancement in testing effectiveness, k-means clustering is limited by the value of k, which will have a great impact on the test effectiveness. To improve the testing effectiveness of these techniques for object-oriented software, in this paper, we propose an approach named Determination Method of Optimal k-value based on the Experimental Process (DMOVk-EP) to determine the optimal k-value of k-means clustering and make the ART algorithms using k-means clustering technique achieve the best fault detection capability. The proposed method consists of two parts, one is a solution model for k based on the experimental process, and the other is an optimal k-value algorithm based on the presented model. We integrate this method with k-means clustering in ART and apply it to a set of open-source programs, with the experimental results showing that our approach obtains much more appropriate k, and also achieves much better testing effectiveness than other related methods.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133218004","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

Multi-objective Search for Model-based Testing 基于模型测试的多目标搜索

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00029

Rui Wang, Cyrille Artho, L. Kristensen, V. Stolz

引用次数: 1

Process-variation Effects on 3D TLC Flash Reliability: Characterization and Mitigation Scheme 工艺变化对3D TLC闪光可靠性的影响:表征和缓解方案

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00051

Yuqian Pan, Haichun Zhang, Mingyang Gong, Zhenglin Liu

引用次数: 7

Attack Graph Auto-Generation for Blockchains based on Bigraphical Reaction Systems 基于图形反应系统的区块链攻击图自动生成

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00046

Lian Yu, Lijun Liu, Yanbing Jiang, Qi Jing, Bei Zhao, Chen Zhang

{"title":"Attack Graph Auto-Generation for Blockchains based on Bigraphical Reaction Systems","authors":"Lian Yu, Lijun Liu, Yanbing Jiang, Qi Jing, Bei Zhao, Chen Zhang","doi":"10.1109/QRS51102.2020.00046","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00046","url":null,"abstract":"Blockchains (BCs) are claimed to have immutability, distributed consensus, established trust, distributed identity and eternal verifiable, and sound like the ultimate security unim-peachable technology. At the time, however, new age security attacks on the key components of BCs are emerging, which are very sophisticated and can cause huge irreparable damages, including network-based attacks, consensus & ledger-based at-tacks, smart contract-based attacks, and wallet-based attacks. This paper proposes to use bigraph theory to model BC attack meta-model, and automatically generate attack graphs for BC security evaluation. Bigraphical sorting mechanism is used to depict configuration of BC systems, and bigraphical reaction rules are designed to characterize attack templates and attacker behaviours. Adaptive exploit flow approach is proposed to reduce the complexity of matching algorithm guided by interested attack exploits, and probability is introduced into bigraphs to measure the capability of attackers. Preliminary experiments have shown the validity of the proposed approach.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116823585","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: 2