Modeling self-adaptive software systems with learning petri nets

Zuohua Ding, Yuan Zhou, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Traditional models have limitation to model adaptive software systems since they build only for fixed requirements, and cannot model the behaviors that change at run-time in response to environmental changes. In this paper, an adaptive Petri net is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) It can model a runtime environment; 2) The components in the model can collaborate to make adaption decisions; and 3) The computing is done at the local, while the adaption is for the whole system. We illustrate the proposed adaptive Petri net by modeling a manufacturing system.

Original languageEnglish (US)
Title of host publication36th International Conference on Software Engineering, ICSE Companion 2014 - Proceedings
PublisherAssociation for Computing Machinery
Pages464-467
Number of pages4
ISBN (Print)9781450327688
DOIs
StatePublished - Jan 1 2014
Event36th International Conference on Software Engineering, ICSE 2014 - Hyderabad, India
Duration: May 31 2014Jun 7 2014

Publication series

Name36th International Conference on Software Engineering, ICSE Companion 2014 - Proceedings

Other

Other36th International Conference on Software Engineering, ICSE 2014
CountryIndia
CityHyderabad
Period5/31/146/7/14

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Adaptive Petri net
  • Adaptive software system
  • Neural network
  • Requirement modeling

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