Software Engineering Internet of Things (SEIOFT)
The approach in this paper will focus on the introduction of information hubs, with facts and knowledge extracted from existing software resources being formally modeled using ontologies and Linked Data representations. In addition the use of automated tagging, machine learning, source code analysis and Semantic Web reasoning will be investigate to further enrich these information hubs with semantic information and semantic traceability links - both being an essential prerequisite for establishing a Software Engineering Web of Things. An initial set of smart workflows and services for the software evolution domain will be introduced that allow for the support of composite applications or task contexts across global heterogeneous knowledge extracted from the software engineering domain. Both, the datasets and smart services being developed, will be publicly available through an online portal, to create an initial version of a Web of Things for the software engineering domain, where information hubs replace traditional information silos found in the software domain. These information hubs (things) will promote knowledge sharing, reuse and integration, by facilitating a crowd-sourcing effort involving the software community at large to share and link their own data (facts and analysis results) and smart services to continue evolving this Web of Things for the software engineering domain.
While many domains (e.g., automotive, manufacturing, home automation) have started to make the Web 3.0 an important and integrated part of their future solution space, the software engineering community has yet to embrace this move. The objective of this research program is to introduce Ambient Software Engineering as a cross disciplinary approach, integrating traditional software engineering (evolution) research, with state-of-the art research from domains such as Semantic Web, Natural Language Processing, knowledge modeling and Mining Software Repositories. The approach takes advantage of matured Semantic Web technologies, which support a standardized unified and formal knowledge representation using ontologies and Linked Data of existing heterogeneous knowledge resources.
SV-AF – A Security Vulnerability Analysis Framework
The globalization of the software industry has introduced a widespread use of system components across traditional system boundaries. Due to this global reuse, also vulnerabilities and security concerns are no longer limited in their scope to individual systems but instead can now affect global software ecosystems. While known vulnerabilities and security concerns are reported in specialized vulnerability databases, these repositories often remain information silos. In this research, we introduce a modeling approach, which eliminates these silos by linking security knowledge with other software artifacts to improve traceability and trust in software products. In our approach, we introduce a Security Vulnerabilities Analysis Framework (SV-AF) to support evidence based vulnerability detection.