Proceedings of the Evaluation and Assessment in Software Engineering最新文献

{"title":"PrivDRM","authors":"T. Gaber, Ali Ahmed, A. Mostafa","doi":"10.1145/3383219.3383289","DOIUrl":"https://doi.org/10.1145/3383219.3383289","url":null,"abstract":"Digital Right Management (DRM) is a technology developed to prevent illegal reproduction and distribution of digital contents. It protects the rights of content owners by allowing only authorised consumers to legitimately access associated digital content. DRM systems typically use a consumer's identity for authentication. In addition, some DRM systems collect consumer's preferences to obtain a content license. Thus, the behaviour of DRM systems disadvantages the digital content consumers (i.e. neglecting consumers' privacy) focusing more on securing the digital content (i.e. biased towards content owners). This paper proposes the Privacy-Preserving Digital Rights Management System (PrivDRM) that allows a consumer to acquire digital content with its license without disclosing complete personal information and without using any third parties. To evaluate the performance of the proposed solution, a prototype of the PrivDRM system has been developed and investigated. The security analysis (attacks and threats) are analysed and showed that PrivDRM supports countermeasures for well-known attacks and achieving the privacy requirements. In addition, a comparison with some well-known proposals shows that PrivDRM outperforms those proposals in terms of processing overhead.","PeriodicalId":318328,"journal":{"name":"Proceedings of the Evaluation and Assessment in Software Engineering","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091484","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}

{"title":"RMDevOps","authors":"S. Rafi, Wu Yu, M. Akbar","doi":"10.1145/3383219.3383278","DOIUrl":"https://doi.org/10.1145/3383219.3383278","url":null,"abstract":"DevOps is a new software engineering paradigm adopted by various software organizations to develop an environment of continuous deployment and delivery within time. Numerous experts are offering their services to help organizations, how to implement DevOps activities in software organization. Though, still there are various issues for software organizations to adopt DevOps activities. To overcome such issues, there must be an approach that could assist software organizations towards better adoption of DevOps activities. The core objective of this research is to design a Readiness Model for DevOps (RMDevOps) to improve the adoption of DevOps activities in a software organization. Based on existing models in other fields of software engineering, we will develop this model. We have conducted a systematic literature review and empirical study on DevOps, for understanding the impact of the success factors of DevOps in the real world and literature. This study covers the first step of development of RMDevOps model, by identifying the success factors of DevOps and presenting the outcomes in the form of robust framework.","PeriodicalId":318328,"journal":{"name":"Proceedings of the Evaluation and Assessment in Software Engineering","volume":"1 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114034409","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}

{"title":"MLCQ","authors":"L. Madeyski, T. Lewowski","doi":"10.1145/3383219.3383264","DOIUrl":"https://doi.org/10.1145/3383219.3383264","url":null,"abstract":"Context Research on code smells accelerates and there are many studies that discuss them in the machine learning context. However, while data sets used by researchers vary in quality, all which we encountered share visible shortcomings---data sets are gathered from a rather small number of often outdated projects by single individuals whose professional experience is unknown. Aim This study aims to provide a new data set that addresses the aforementioned issues and, additionally, opens new research opportunities. Method We collaborate with professional software developers (including the code quest company behind the codebeat automated code review platform integrated with GitHub) to review code samples with respect to bad smells. We do not provide additional hints as to what do we mean by a given smell, because our goal is to extract professional developers' contemporary understanding of code smells instead of imposing thresholds from the legacy literature. We gather samples from active open source projects manually verified for industry-relevance and provide repository links and revisions. Records in our MLCQ data set contain the type of smell, its severity and the exact location in source code, but do not contain any source code metrics which can be calculated using various tools. To open new research opportunities, we provide results of an extensive survey of developers involved in the study including a wide range of details concerning their professional experience in software development and many other characteristics. This allows us to track each code review to the developer's background. To the best of our knowledge, this is a unique trait of the presented data set. Conclusions The MLCQ data set with nearly 15000 code samples was created by software developers with professional experience who reviewed industry-relevant, contemporary Java open source projects. We expect that this data set should stay relevant for a longer time than data sets that base on code released years ago and, additionally, will enable researchers to investigate the relationship between developers' background and code smells' perception.","PeriodicalId":318328,"journal":{"name":"Proceedings of the Evaluation and Assessment in Software Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114454116","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}