Past Projects

In this research, we do promote Linked Data to facilitate knowledge and result sharing across datasets. We do believe, changing the underlying knowledge model to an ontological representation, can not only overcome existing knowledge sharing challenges, but also promote stronger collaboration and result integration among research groups and tool developers in the software analysis domain. So far, more than one hundred official Linked Data datasets have been published online. The official Linking Open Data Cloud (LOD) Linked Data Cloud shows these datasets and their connections (September 2011). Government data, bioinformatics, news, scientific publications and general purpose knowledgebase (e.g., Wikipedia and OpenCyc) constitute major themes within the cloud. Notably, there has been no dataset related to software analysis research except our SeCold dataset. We do believe that like in other domains, Linked Data will become a de facto standard for modeling and sharing knowledge and facts across datasets in the software analysis domain.
For more information on SECOLD please visit our SECOLD project homepage at: or contact Iman Keivanloo, who came up with the idea and created SECOLD as part of his Ph.D. Furthermore, he also created or was directly involved in all the SECOLD related sub projects.

SECOLD provides also the enabling infrastructures for the following sub projects which build upon SECOLD and its large Internet-scale data set.

Quality has become a key assessment factor for organizations to determine if their software ecosystems are capable to meet constantly changing environmental factors and requirements. Many quality models exist to assess the evolvability or maintainability of software systems. Common to these models is that they, contrary to the software ecosystems they are assessing, are not evolvable or reusable. In this research, we introduce SE-EQUAM a novel ontology-based quality assessment metamodel that was designed from ground up to support model reuse and evolvability. SE-EQUAM takes advantage of Semantic Web technologies such as support for the open world assumption, incremental knowledge population, and knowledge inference. As part of this project we also investigate an interdisciplinary approach to quality prediction, combining trend analysis techniques and patterns from the financial market domain and re-apply them on the software domain. Click on picture for full-size version.
SE-EQUAM - Evolvable Quality Assessment Model                       Stockmarket  tehnical indicators