Talks and Poster Presentations (with Proceedings-Entry):
F. Ekaputra, E. Serral Asensio, S. Biffl:
"Building an Empirical Software Engineering Research Knowledge Base from Heterogeneous Data Sources";
Talk: 14th International Conference on Knowledge Technologies and Data-Driven Business (I-KNOW),
- 2014-09-19; in: "Proceedings of the 14th International Conference on Knowledge Technologies and Data-Driven Business (I-KNOW)",
Recently, the Systematic Knowledge Engineering (SKE) process
has been introduced to help researchers build up an empirical
software engineering (EMSE) Body of Knowledge (BoK) based
on a systematic literature review process. However, the SKE process
does not explain how to effectively capture and represent the
EMSE knowledge to enable efficient data analysis. In this paper,
we introduce the EMSE Research Knowledge Base Building
(RKB) process, which guides knowledge engineers in developing
and using a knowledge base (KB) for the SKE process based on
contributions from heterogeneous data sources. We evaluate the
RKB process in the context of three research topics from the EMSE
domain: software inspection experiments, theory construct
identification, and threats to validity. Major results are that the
RKB process is effective in guiding the knowledge engineer to
build a KB that allows answering the EMSE-specific queries. The
RKB process shows promising results in the EMSE research context
and should be investigated in other research contexts as well.
Design and architecture of data sharing facilities, Empirical Software Engineering, Systematic Knowledge Engineering Process, Metadata representation, Digital research libraries, Science 2.0.
Created from the Publication Database of the Vienna University of Technology.