Semantic analysis of software specifications with Linked Data

Semantic analysis of software specifications with Linked Data

Software development life cycle is the process involved in the design, development and improvement of a software application. Nowadays especially component systems are used due to possibility of implementing reusable independent modules. The individual module, known as a software component, can be implemented in a form of a software package, a web service or a web resource that encapsulates a set of related functions. Software products are derived in a configuration process by composing different components. Moreover a software product line enables stakeholders to derive a different software products based on their needs. This fact and need for validation of the software product and its components requires methods for software specification processing and matching with concrete sw properties. In this article we will propose an approach for semantic analysis of software specifications with Linked Data.

Keywords: Software Specifications, Linked Data, Semantic Analysis

Year: 2014

Journal ISSN: 1992-8645
Download: download Full text 

Authors of this publication:


Martin Dostal


E-mail: madostal@kiv.zcu.cz

Martin graduated from the University of West Bohemia in 2009, specialized in software engineering. He is interested in the semantic Web, information retrieval, and question answering.

Michal Nykl


E-mail: nyklm@kiv.zcu.cz
WWW: http://home.zcu.cz/~nyklm/

Michal is researcher at the Department of Computer Science and Engineering at the University of West Bohemia in Pilsen (Czech Republic). He is Software engineer and his interest is focused on the Graph structure mining algorithms, which are used for Social network analysis, Text-mining, NPL and similar problems.

Karel Je┼żek


Phone:  +420 377632475, 377632400
E-mail: jezek_ka@kiv.zcu.cz
WWW: http://www-kiv.zcu.cz/~jezek_ka/

Karel is a group coordinator and a supervisor of PhD students working at research projects of this Group.

Related Projects:


Project

Document Clustering and Linked Data

Authors:  Karel Je┼żek, Martin Dostal
Desc.:Unsupervised methods for automatic tagging and clustering based on information extraction from Linked data.