Bibliographic Details
| Title: |
A survey and comparison of transformation tools based on the transformation tool contest. |
| Authors: |
Jakumeit, Edgar eja@ipd.info.uni-karlsruhe.de, Buchwald, Sebastian, Wagelaar, Dennis, Dan, Li, Hegedüs, Ábel, Herrmannsdörfer, Markus, Horn, Tassilo, Kalnina, Elina, Krause, Christian, Lano, Kevin, Lepper, Markus post@markuslepper.eu, Rensink, Arend, Rose, Louis, Wätzoldt, Sebastian, Mazanek, Steffen steffen.mazanek@gmail.com |
| Source: |
Science of Computer Programming. Jun2014, Vol. 85 Issue Part A, p41-99. 59p. |
| Subjects: |
Computer surveys, Mathematical transformations, Computer engineering, Matching theory, Graph theory, Data structures |
| Abstract: |
Abstract: Model transformation is one of the key tasks in model-driven engineering and relies on the efficient matching and modification of graph-based data structures; its sibling graph rewriting has been used to successfully model problems in a variety of domains. Over the last years, a wide range of graph and model transformation tools have been developed – all of them with their own particular strengths and typical application domains. In this paper, we give a survey and a comparison of the model and graph transformation tools that participated at the Transformation Tool Contest 2011. The reader gains an overview of the field and its tools, based on the illustrative solutions submitted to a Hello World task, and a comparison alongside a detailed taxonomy. The article is of interest to researchers in the field of model and graph transformation, as well as to software engineers with a transformation task at hand who have to choose a tool fitting to their needs. All solutions referenced in this article provide a SHARE demo. It supported the peer-review process for the contest, and now allows the reader to test the tools online. [Copyright &y& Elsevier] |
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Copyright of Science of Computer Programming is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
