A customizable pattern-based software process simulation model: design, calibration and application

  • Authors:

  • Vahid Garousi;Keyvan Khosrovian;Dietmar Pfahl

  • Affiliations:

  • Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Canada;Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Canada;Dept. of Elec. and Comp. Eng., Schulich Sch. of Eng., Univ. of Calgary, Canada and Simula Res. Lab., Software Eng. Dept., Lysaker, Norway and Department of Informatics, University of Oslo, Norway

  • Venue:
  • Software Process: Improvement and Practice - Examining Process Design and Change
  • Year:
  • 2009

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Abstract

Software process analysis and improvement relies heavily on empirical research. Empirical research requires measurement, experimentation, and modeling. However, whatever evidence is gained via empirical research is strongly context dependent. Thus, it is hard to combine results and capitalize upon them for the purpose of improvement in evolving development environments. The process simulation model GENSIM 2.0 addresses these challenges. GENSIM 2.0 is a generic process simulation tool representing V-model type software development processes. Compared to existing process simulation models in the literature, the novelty of GENSIM 2.0 is twofold. Firstly, its model structure is customizable to organization-specific processes. This is achieved by using a limited set of generic structures (macro-patterns). Secondly, its model parameters can be easily calibrated to available empirical data and expert knowledge. This is achieved by making the internal model structures explicit and by providing guidance on how to calibrate model parameters. This article outlines the structure of GENSIM 2.0, gives examples on how to calibrate the model to available empirical data, and demonstrates its usefulness through two application scenarios The first scenario illustrates how GENSIM 2.0 helps in finding effective combinations of verification and validation techniques under given time and effort constraints. The second scenario shows how the simulator supports in finding the best combination of alternative verification techniques. Copyright © 2009 John Wiley & Sons, Ltd.