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Collective Intelligence-Based Quality Assurance: Combining Inspection and Risk Assessment to Support Process Improvement in Multi-Disciplinary Engineering

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Book cover Systems, Software and Services Process Improvement (EuroSPI 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 633))

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Abstract

In Multi-Disciplinary Engineering (MDE) environments, engineers coming from different disciplines have to collaborate. Typically, individual engineers apply isolated tools with heterogeneous data models and strong limitations for collaboration and data exchange. Thus, projects become more error-prone and risky. Although Quality Assurance (QA) methods help to improve individual engineering artifacts, results and experiences from previous activities remain unused. This paper describes a Collective Intelligence-Based Quality Assurance (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 artifacts 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.

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Notes

  1. 1.

    Software tool Jira: https://www.atlassian.com/software/jira.

  2. 2.

    Gerrit: https://www.gerritcodereview.com.

  3. 3.

    GitHub: https://github.com.

  4. 4.

    Linear scale for probability, severity, and detectability: 0 stands for very low probability, severity, and detectability; 10 indicates critical probability, severity, and detectability. For example, the rating 10/10/10 means that candidate defects will definitely be in the final product (high probability) with a very critical impact (high severity) and it is very hard to identify the defect early (high detectability).

  5. 5.

    Eclipse Marketplace: https://marketplace.eclipse.org.

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Acknowledgements

Parts of this work were supported by the Christian Doppler Forschungsgesellschaft, the Federal Ministry of Economy, Family and Youth, the Austrian National Foundation for Research, Technology and Development, and the TU Wien Doctoral College on Cyber-Physical Production Systems.

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Authors and Affiliations

  1. SBA Research gGmbH, Favoritenstrasse 16, 1040, Vienna, Austria

    Dietmar Winkler

  2. Institute of Software Technology and Interactive Systems, CDL-Flex, Vienna University of Technology, Favoritenstrasse 9/188, 1040, Vienna, Austria

    Dietmar Winkler, Juergen Musil, Angelika Musil & Stefan Biffl

Authors
  1. Dietmar Winkler
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  2. Juergen Musil
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  3. Angelika Musil
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  4. Stefan Biffl
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Corresponding author

Correspondence to Dietmar Winkler .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Christian Kreiner

  2. Dublin City University, Dublin 9, Ireland

    Rory V. O'Connor

  3. Volkswagen AG, Wolfsburg, Germany

    Alexander Poth

  4. I.S.C.N. GesmbH, Graz, Austria

    Richard Messnarz

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Winkler, D., Musil, J., Musil, A., Biffl, S. (2016). Collective Intelligence-Based Quality Assurance: Combining Inspection and Risk Assessment to Support Process Improvement in Multi-Disciplinary Engineering. In: Kreiner, C., O'Connor, R., Poth, A., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2016. Communications in Computer and Information Science, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-44817-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-44817-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44816-9

  • Online ISBN: 978-3-319-44817-6

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