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Rapid performance modeling by transforming use case maps to palladio component models

Published: 21 April 2013 Publication History

Abstract

Complex information flows in the domain of industrial software systems complicate the creation of performance models to validate the challenging performance requirements. Performance models using annotated UML diagrams or mathematical notations are difficult to discuss with stakeholders from the industrial automation domain, who often have a limited software engineering background. We introduce a novel model transformation from Use Case Maps (UCM) to the Palladio Component Model (PCM), which enables performance modeling based on an intuitive notation for complex information flows. The resulting models can be solved using existing simulators or analytical solvers. We validated the correctness of the transformation with three case study models, and performed a user study. The results showed a performance prediction deviation of less than 10 percent compared to a reference model in most cases.

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cover image ACM Conferences
ICPE '13: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
April 2013
446 pages
ISBN:9781450316361
DOI:10.1145/2479871
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 April 2013

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Author Tags

  1. empirical study
  2. model transformation
  3. palladio
  4. performance prediction
  5. use case maps

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ICPE '13 Paper Acceptance Rate 28 of 64 submissions, 44%;
Overall Acceptance Rate 252 of 851 submissions, 30%

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