RuleMerger: Automatic construction of variability-based model transformation rules (bibtex)
by StrĂ¼ber, Daniel, Rubin, Julia, Arendt, Thorsten, Chechik, Marsha and Taentzer, Gabriele
Abstract:
Unifying similar model transformation rules into variabilitybased ones can improve both the maintainability and the performance of a model transformation system. Yet, manual identification and unification of such similar rules is a tedious and error-prone task. In this paper, we propose a novel merge-refactoring approach for automating this task. The approach employs clone detection for identifying overlapping rule portions and clustering for selecting groups of rules to be unified. Our instantiation of the approach harnesses state-of-the-art clone detection and clustering techniques and includes a specialized merge construction algorithm. We formally prove correctness of the approach and demonstrate its ability to produce high-quality outcomes in two real-life casestudies. \textcopyright Springer-Verlag Berlin Heidelberg 2016.
Reference:
RuleMerger: Automatic construction of variability-based model transformation rules (StrĂ¼ber, Daniel, Rubin, Julia, Arendt, Thorsten, Chechik, Marsha and Taentzer, Gabriele), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 9633, 2016.
Bibtex Entry:
@InProceedings{Strueber2016b,
  author        = {Str{\"{u}}ber, Daniel and Rubin, Julia and Arendt, Thorsten and Chechik, Marsha and Taentzer, Gabriele and Pl{\"{o}}ger, Jennifer},
  title         = {{RuleMerger: Automatic construction of variability-based model transformation rules}},
  booktitle     = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2016},
  volume        = {9633},
  pages         = {122--140},
  __markedentry = {[piets:1]},
  abstract      = {Unifying similar model transformation rules into variabilitybased ones can improve both the maintainability and the performance of a model transformation system. Yet, manual identification and unification of such similar rules is a tedious and error-prone task. In this paper, we propose a novel merge-refactoring approach for automating this task. The approach employs clone detection for identifying overlapping rule portions and clustering for selecting groups of rules to be unified. Our instantiation of the approach harnesses state-of-the-art clone detection and clustering techniques and includes a specialized merge construction algorithm. We formally prove correctness of the approach and demonstrate its ability to produce high-quality outcomes in two real-life casestudies. {\textcopyright} Springer-Verlag Berlin Heidelberg 2016.},
  doi           = {10.1007/978-3-662-49665-7_8},
  isbn          = {9783662496640},
  issn          = {16113349},
  keywords      = {moca},
  mendeley-tags = {moca},
  url           = {http://dx.doi.org/10.1007/978-3-662-49665-7_8},
}
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