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Efficient evaluation of CSAN models by state space analysis methods

Abdollahi Azgomi, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1109/ICSEA.2006.261313
  3. Publisher: IEEE Computer Society , 2006
  4. Abstract:
  5. We have recently introduced a high-level extension for stochastic activity networks (SANs) called coloured stochastic activity networks (CSANs). CSANs have several distinguishing properties, which make them quite appropriate for modeling and evaluation of software performance and dependability. CSANs have introduced a construct called coloured place for data manipulation. A coloured place holds a list of tokens of a userdefined token type. CSAN models can be evaluated by state space analysis techniques or discrete-event simulation. However, their state spaces will become very large, even for a small CSAN model. For efficient evaluation of these models by state space analysis methods, we will introduce measure-adaptive state space analysis process in this paper. Based on this method, it is possible to construct high-level CSAN models. However, for efficient evaluation, it is possible to generate and analyze a reduced state space based on user-specified performance or dependability measures. © 2006 IEEE
  6. Keywords:
  7. Computer software ; Discrete event simulation ; Petri nets ; State space methods ; Stochastic models ; User interfaces ; Colored stochastic activity networks ; Performance evaluation ; State space analysis techniques ; Data structures
  8. Source: 2006 International Conference on Software Engineering Advances, ICSEA'06, Tahiti, 29 October 2006 through 3 November 2006 ; 2006 , Pages 57-62 ; 0769527035 (ISBN); 9780769527031 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4031842