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Common Pitfalls of Using QVT Relations - Graphical Debugging as Remedy

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Abstract

OMG’s Model-Driven Architecture (MDA) has emerged as a new approach for the development of software. For this, the Query/View/Transformation (QVT) standard plays a central role, since it allows for the specification of model transformations. Nevertheless, until now, QVT-tool support in general and debugging support in particular in the context of MDA are rather limited, supposable being a reason, that the adoption of QVT in practice has not yet been achieved. We therefore propose graphical debugging for the QVT Relations language based on TROPIC - a model transformation approach on the basis of Coloured Petri Nets. By enabling debugging on the TROPIC level, one gains several advantages when developing transformations. Firstly, debugging can take place at a high level of abstraction. Secondly, it serves for explicating the operational semantics of a transformation. Thirdly, it provides a homogenous representation of all transformation artifacts. As a first step towards QVT debugging, this paper aims at a deeper understanding of the operational semantics of QVT, classifying common pitfalls by using QVT and discussing how they may be identified at the TROPIC level.
Original languageEnglish
Title of host publicationProceedings of the 14th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS 2009)
Pages329-334
Number of pages6
DOIs
Publication statusPublished - 2009

Publication series

NameProceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
ISSN (Print)2770-8527
ISSN (Electronic)2770-8535

Fields of science

  • 101004 Biomathematics
  • 101027 Dynamical systems
  • 101028 Mathematical modelling
  • 101029 Mathematical statistics
  • 101014 Numerical mathematics
  • 101015 Operations research
  • 101016 Optimisation
  • 101017 Game theory
  • 101018 Statistics
  • 101019 Stochastics
  • 101024 Probability theory
  • 101026 Time series analysis
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102004 Bioinformatics
  • 102013 Human-computer interaction
  • 102018 Artificial neural networks
  • 102019 Machine learning
  • 103029 Statistical physics
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 202017 Embedded systems
  • 202035 Robotics
  • 202036 Sensor systems
  • 202037 Signal processing
  • 305901 Computer-aided diagnosis and therapy
  • 305905 Medical informatics
  • 305907 Medical statistics
  • 102032 Computational intelligence
  • 102033 Data mining
  • 101031 Approximation theory
  • 102002 Augmented reality
  • 102006 Computer supported cooperative work (CSCW)
  • 102015 Information systems
  • 102021 Pervasive computing
  • 102025 Distributed systems
  • 102027 Web engineering
  • 202038 Telecommunications

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