Instant and incremental QVT transformation for runtime models

  • Authors:
  • Hui Song;Gang Huang;Franck Chauvel;Wei Zhang;Yanchun Sun;Weizhong Shao;Hong Mei

  • Affiliations:
  • Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies (Ministry of Education), School of Electronic Engineering & Computer Science, Peking University, China

  • Venue:
  • Proceedings of the 14th international conference on Model driven engineering languages and systems
  • Year:
  • 2011

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Abstract

As a dynamic representation of the running system, a runtime model provides a model-based interface to monitor and control the system. A key issue for runtime models is to maintain their causal connections with the running system. That means when the systems change, the models should change accordingly, and vice versa. However, for the abstract runtime models that are heterogeneous to their target systems, it is challenging to maintain such causal connections. This paper presents a model-transformation-based approach to maintaining causal connections for abstract runtime models. We define a new instant and incremental transformation semantics for the QVT-Relational language, according to the requirements of runtime models, and develop the transformation algorithm following this semantics. We implement this approach on the mediniQVT transformation engine, and apply it to provide the runtime model for an intelligent office system named SmartLab.