CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

  • Authors:

  • Gisèle Douta;Haydar Talib;Oscar Nierstrasz;Frank Langlotz

  • Affiliations:

  • MEM Research Center for Orthopaedic Surgery, Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014 Bern, Switzerland;MEM Research Center for Orthopaedic Surgery, Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014 Bern, Switzerland;Software Composition Group, Institute of Computer Science, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;MEM Research Center for Orthopaedic Surgery, Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014 Bern, Switzerland

  • Venue:
  • Information and Software Technology
  • Year:
  • 2009

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Abstract

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.