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

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 system Defect detection Engineering process Improvement FMEA Inspection Review Risk 

References

  1. 1.
    Kovalenko, O., Winkler, D., Kalinowski, M., Serral, E., Biffl, S.: Engineering process improvement in heterogeneous multi-disciplinary environments with defect causal analysis. In: Barafort, B., O’Connor, R.V., Poth, A., Messnarz, R. (eds.) EuroSPI 2014. CCIS, vol. 425, pp. 73–85. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Biffl, S., Lüder, A., Winkler, D.: Multi-disciplinary engineering for industrie 4.0: semantic challenges, needs, and capabilities. In: Biffl, S., Sabou, M. (eds.) Semantic Web for Intelligent Engineering Applications, Chap. 2. Springer (2016, to appear)Google Scholar
  3. 3.
    Biffl, S., Moser, T., Winkler, D.: Risk assessment in multi-disciplinary (Software +) engineering projects. IJSEKE 21(2), 211–236 (2011). SI on SW Risk AssessmentGoogle Scholar
  4. 4.
    Aurum, A., Petersson, H., Wohlin, C.: State-of-the-art: software inspection after 25 years. J. Softw. Test. Verification Reliab. 12(3), 133–154 (2002)CrossRefGoogle Scholar
  5. 5.
    Wiegers, K.: Peer Reviews in Software: A Practical Guide. Addison-Wesley, Boston (2001)Google Scholar
  6. 6.
    Broekman, B., Notenboom, E.: Testing Embedded Software. Addison Wesley, Boston (2002)Google Scholar
  7. 7.
    Myers, G.J., Sandler, C., Badgett, T.: The Art of Software Testing. Wiley, New York (2011)Google Scholar
  8. 8.
    Stamatis, D.H.: Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press, Milwaukee (2003)Google Scholar
  9. 9.
    Teng, S.-H., Shin-Yann, H.: Failure mode and effects analysis: an integrated approach for product design and process control. J Qual. Reliab. Mgmt. 13(5), 8–26 (1996)CrossRefGoogle Scholar
  10. 10.
    Ericson, C.A.: Fault Tree Analysis Primer. CreateSpace Independent Publishing, Seattle (2011)Google Scholar
  11. 11.
    Kalinowski, M., Card, D.N., Travassos, G.H.: Evidence-based guidelines to defect causal analysis. IEEE Softw. 29(4), 16–18 (2012)CrossRefGoogle Scholar
  12. 12.
    Musil, J., Musil, A., Weyns, D., Biffl, S.: An architecture framework for collective intelligence systems. In: 12th Working IEEE/IFIP Conference on Software Architecture (WICSA), pp. 21–30. IEEE (2015)Google Scholar
  13. 13.
    Musil, J., Musil, A., Biffl, S.: Introduction and challenges of environment architectures for collective intelligence systems. In: Weyns, D., et al. (eds.) E4MAS 2014 - 10 years later. LNCS, vol. 9068, pp. 76–94. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23850-0_6 CrossRefGoogle Scholar
  14. 14.
    Laitenberger, O., DeBaud, J.-M.: An encompassing life cycle centric survey of software inspection. J. Syst. Softw. 50(1), 5–31 (2000)CrossRefGoogle Scholar
  15. 15.
    Carver, J., Shull, F., Basili, V.: Can observational techniques help novices overcome the software inspection learning curve? An empirical investigation. ESE J. 11(4), 523–539 (2006)Google Scholar
  16. 16.
    Kollanus, S., Koskinen, J.: Survey of Software Inspection Research: 1991–2005, Working Papers WP-40, University of Jyväskylä (2007)Google Scholar
  17. 17.
    Travassos, G., Shull, F., Fredericks, M., Basili, V.R.: Detecting defects in object-oriented designs: using reading techniques to increase software quality. ACM SIGPLAN Not. 34(10), 47–56 (1999). ACMCrossRefGoogle Scholar
  18. 18.
    Biffl, S.: Inspection Techniques to support Project and Quality Management. Vienna University of Technology, Shaker, Maastricht (2001). HabilitationMATHGoogle Scholar
  19. 19.
    Shull, F., Rus, I., Basili, V.R.: How perspective-based reading can improve requirements inspection. IEEE Comput. 33(7), 73–79 (2002)CrossRefGoogle Scholar
  20. 20.
    Winkler, D., Biffl, S.: Focused inspections to support defect detection in multi-disciplinary engineering environments. In: 16th International Conference on Product-Focused Software Process Improvement, Research Preview Paper (2015)Google Scholar
  21. 21.
    Blackwell, A., Green, T.: Notational Systems – The Cognitive Dimensions of Notations Framework, HCI Models, Theories, and Frameworks: Toward an Interdisciplinary Science. Morgan Kaufmann, San Francisco (2003)Google Scholar
  22. 22.
    Moser, T., Mordinyi, R., Winkler, D., Melik-Merkumians, M., Biffl, S.: Efficient automation systems engineering process support based on semantic integration of engineering knowledge. In: 16th International Conference on Emerging Technologies. and Factory Automation (ETFA) (2011)Google Scholar
  23. 23.
    Novak, P., Serral, E., Mordinyi, R., Sindelar, R.: Integrating heterogeneous engineering knowledge and tools for efficient industrial simulation model support. Adv. Eng. Inf. 29(3), 575–590 (2015)CrossRefGoogle Scholar

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

Personalised recommendations