Automating Cross-Disciplinary Defect Detection in Multi-Disciplinary Engineering Environments

O. Kovalenko, E. Serral Asensio, M. Sabou, F. Ekaputra, D. Winkler, S. Biffl:
"Automating Cross-Disciplinary Defect Detection in Multi-Disciplinary Engineering Environments";
Vortrag: 19th Conference on Knowledge Engineering and Knowledge Management (EKAW), Linköping,Sweden; 24.11.2014 - 28.11.2014; in:"Knowledge Engineering and Knowledge Management", Springer, (2014), ISBN: 978-3-319-13703-2.

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Multi-disciplinary engineering (ME) projects are conducted in com-plex heterogeneous environments, where participants, originating from different disciplines, e.g., mechanical, electrical, and software engineering, collaborate to satisfy project and product quality as well as time constraints. Detecting defects across discipline boundaries early and efficiently in the engineering process is a challenging task due to heterogeneous data sources. In this paper we explore how Semantic Web technologies can address this challenge and present the On-tology-based Cross-Disciplinary Defect Detection (OCDD) approach that sup-ports automated cross-disciplinary defect detection in ME environments, while allowing engineers to keep their well-known tools, data models, and their cus-tomary engineering workflows. We evaluate the approach in a case study at an industry partner, a large-scale industrial automation software provider, and re-port on our experiences and lessons learned. Major result was that the OCDD approach was found useful in the evaluation context and more efficient than manual defect detection, if cross-disciplinary defects had to be handled.