European Conference on Software Process Improvement

EuroSPI 2016: Systems, Software and Services Process Improvement pp 163-175

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

  • Dietmar Winkler
  • Juergen Musil
  • Angelika Musil
  • Stefan Biffl
Conference paper

DOI: 10.1007/978-3-319-44817-6_13

Volume 633 of the book series Communications in Computer and Information Science (CCIS)
Cite this paper as:
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

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.

Keywords

Collective intelligence systemDefect detectionEngineering processImprovementFMEAInspectionReviewRisk

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dietmar Winkler
    • 1
    • 2
  • Juergen Musil
    • 2
  • Angelika Musil
    • 2
  • Stefan Biffl
    • 2
  1. 1.SBA Research gGmbHViennaAustria
  2. 2.Institute of Software Technology and Interactive Systems, CDL-FlexVienna University of TechnologyViennaAustria