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An empirical analysis of vulnerabilities in virtualization technologies
(2016) Gkortzis, Antonios; Rizou, Stamatia; Spinellis, Diomidis
Cloud computing relies on virtualization technologies to provide computer resource elasticity and scalability. Despite its benefits, virtualization technologies come with serious concerns in terms of security. Although existing work focuses on specific vulnerabilities and attack models related to virtualization, a systematic analysis of known vulnerabilities for different virtualization models, including hypervisor-based and container-based solutions is not present in the literature. In this paper, we present an overview of the existing known vulnerabilities for hypervisor and container solutions reported in the CVE database and classified under CWE categories. Given the vulnerability identification and categorization, we analyze our results with respect to different virtualization models and license schemes (open source/commercial). Our findings show among others that hypervisors and containers share common weaknesses with most of their vulnerabilities reported in the category of security features.
Research priorities in the area of software technologies
(2017) Spinellis, Diomidis
Ever more products, services, and entire industries, existing ones as well as new, are running on software. This report, based on studies surveying the evolution of technology as well as journal articles, conference papers, and talks covering the future of software engineering, argues that significant investment in software engineering research can help Europe stay on top and even lead a world that is increasingly defined and shaped by software. The need for targeted research in software engineering is prompted by developments in three broad areas. First, the computing landscape is changing from top to bottom. Second, seven software-driven vertical application domains are reshaping entire industries and society as a whole. These domains are autonomous vehicles, massive open online courses, open intellectual property, the Internet of Things, life sciences, 3D printing, financial technology, and Industry 4.0. Third, the computing landscape’s technological trends and the changes in the vertical application domains, give rise to several critical crosscutting software engineering challenges. Software engineering research in the areas of software construction, software design, and software engineering process must be funded as a specific priority, so that it can act as a foundation for the robust evolution of computing and its applications. The findings of such research increase the IT industry’s efficiency and benefit society through more and higher quality software. Europe can build on its world-class pockets of excellence in specific areas of economic activity, such as automotive manufacturing, engineering, aerospace technology, financial services, and luxury goods marketing. Thus, focused, significant, and effective research funding in the area of software engineering can help the development of new methods, tools, architectures, systems, business models, processes, and applications that can be instrumental in the establishment of Europe as a center for the rise of a software-run economy.
Standing on shoulders or feet? An extended study on the usage of the MSR data papers
(2020) Kotti, Zoe; Dritsa, Konstantina; Spinellis, Diomidis; Kravvaritis, Konstantinos
The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. The objective of this study is to examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Overall, data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, by promoting open science initiatives, and by providing incentives for the enrichment of existing data collections.
Word embeddings for the software engineering domain
(2018) Efstathiou, Vasiliki; Chatzilenas, Christos; Spinellis, Diomidis
The software development process produces vast amounts of textual data expressed in natural language. Outcomes from the natural language processing community have been adapted in software engineering research for leveraging this rich textual information;these include methods and readily available tools, often furnished with pre–trained models. State of the art pre–trained models however,capture general, common sense knowledge, with limited value when it comes to handling data specific to a specialized domain.There is currently a lack of domain-specific pre–trained models that would further enhance the processing of natural language artefacts related to software engineering. To this end, we release a word2vecmodel trained over 15GB of textual data from Stack Overflow posts.We illustrate how the model disambiguates polysemous words by interpreting them within their software engineering context. In addition, we present examples of fine-grained semantics captured by the model, that imply transferability of these results to diverse,targeted information retrieval tasks in software engineering and motivate for further reuse of the model.