On the Quantitative Analysis of Agent-Oriented Models

  • Xavier Franch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4001)


Agent-oriented models are used in organization and information system modelling for providing intentional descriptions of processes as a network of relationships among actors. As such, they capture and represent goals, dependencies, intentions, beliefs, alternatives, etc., which appear in several contexts: business process reengineering, information system development, etc. In this paper, we are interested in the definition of a framework for the analysis of the properties that these models exhibit. Indicators and metrics for these properties are defined in terms of the model elements (e.g., actors, dependencies, scenario paths, etc.) Our approach is basically quantitative in nature, which allows defining indicators and metrics that can be reused in many contexts. However, a qualitative component can be introduced if trustable expert knowledge is available; the extent up to which quantitative and qualitative aspects are intertwined can be determined in every single case. We apply our proposal to the i* notation and we take as main case study a highly-intentional property, predictability of model elements.


Expert Judgement Requirement Engineer Intentional Element Strategic Rationale Goal Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xavier Franch
    • 1
  1. 1.Universitat Politècnica de Catalunya (UPC)BarcelonaSpain

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