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Talks and Poster Presentations (with Proceedings-Entry):

D. Winkler, J. Musil, A. Musil, S. Biffl:
"Collective Intelligence-Based Quality Assurance: Combining Inspection and Risk Assessment to Support Process Improvement in Multi-Disciplinary Engineering";
Talk: 23rd EuroSPI Conference, Graz; 2016-09-14 - 2016-09-16; in: "Proceedings of the 23rd EuroSPI Conference, Communications in Computer and Information Science", Springer, (2016), ISBN: 978-3-319-44816-9; 163 - 175.



English abstract:
In Multi-Disciplinary Engineering (MDE) environments, engineers coming from different disciplines have to collaborate. Typically, individual en-gineers apply isolated tools with heterogeneous data models and strong limita-tions for collaboration and data exchange. Thus, projects become more error-prone and risky. Although Quality Assurance (QA) methods help to improve in-dividual engineering artifacts, results and experiences from previous activities remain unused. This paper describes a Collective Intelligence-Based Quality As-surance (CI-Based QA) approach that combines two established QA approaches, i.e., (Software) Inspection and the Failure Mode and Effect Analysis (FMEA), supported by a Collective Intelligence System (CIS) to improve engineering arti-facts and processes based on reusable experience. CIS can help to bridge the gap between inspection and FMEA by collecting and exchanging previously isolated knowledge and experience. The conceptual evaluation with industry partners showed promising results of reusing experience and improving quality assurance performance as foundation for engineering process improvement.In Multi-Disciplinary Engineering (MDE) environments, engineers coming from different disciplines have to collaborate. Typically, individual en-gineers apply isolated tools with heterogeneous data models and strong limita-tions for collaboration and data exchange. Thus, projects become more error-prone and risky. Although Quality Assurance (QA) methods help to improve in-dividual engineering artifacts, results and experiences from previous activities remain unused. This paper describes a Collective Intelligence-Based Quality As-surance (CI-Based QA) approach that combines two established QA approaches, i.e., (Software) Inspection and the Failure Mode and Effect Analysis (FMEA), supported by a Collective Intelligence System (CIS) to improve engineering arti-facts and processes based on reusable experience. CIS can help to bridge the gap between inspection and FMEA by collecting and exchanging previously isolated knowledge and experience. The conceptual evaluation with industry partners showed promising results of reusing experience and improving quality assurance performance as foundation for engineering process improvement.

Keywords:
Collective Intelligence System, Defect Detection, Engineering Pro-cess, Improvement, FMEA, Inspection, Review, Risk.

Created from the Publication Database of the Vienna University of Technology.