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ArcheOpterix: An extendable tool for architecture optimization of AADL models

  • Aldeida Aleti
  • , Stefan Björnander
  • , Lars Grunske
  • , Indika Meedeniya

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

For embedded systems quality requirements are equally if not even more important than functional requirements. The foundation for the fulfillment of these quality requirements has to be set in the architecture design phase. However, finding a suitable architecture design is a difficult task for software and system architects. Some of the reasons for this are an ever-increasing complexity of today's systems, strict design constraints and conflicting quality requirements. To simplify the task, this paper presents an extendable Eclipse-based tool, called ArcheOpterix, which provides a framework to implement evaluation techniques and optimization heuristics for AADL specifications. Currently, evolutionary strategies have been implemented to identify optimized deployment architectures with respect to multiple quality objectives and design constraints. Experiments with a set of initial deployment architectures provide evidence that the tool can successfully find architecture specifications with better quality.

Original languageEnglish
Title of host publicationProceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software, MOMPES 2009
Pages61-71
Number of pages11
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventInternational Workshop on Model-based Methodologies for Pervasive and Embedded Software 2009 - Vancouver, BC, Canada
Duration: 16 May 200916 May 2009

Conference

ConferenceInternational Workshop on Model-based Methodologies for Pervasive and Embedded Software 2009
Abbreviated titleMOMPES 2009
Country/TerritoryCanada
CityVancouver, BC
Period16/05/0916/05/09

Keywords

  • AADL
  • ArcheOpterix
  • Architecture and deployment architecture optimization
  • Evolutionary algorithms
  • Multi-objective and pareto optimization

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