Name
Abstract | ||
|---|---|---|
Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00165-017-0441-3 | Formal Asp. Comput. |
Keywords | Field | DocType |
Model transformation, Graph transformation, Variability, Category theory | ENCODE,Model transformation,Computer science,Correctness,Theoretical computer science,Category theory,Graph rewriting,Merge (version control) | Journal |
Volume | Issue | ISSN |
30 | 1 | 0934-5043 |
Citations | PageRank | References |
4 | 0.38 | 46 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Strüber | 1 | 116 | 21.50 |
| Julia Rubin | 2 | 207 | 11.11 |
| Thorsten Arendt | 3 | 233 | 11.76 |
| Marsha Chechik | 4 | 2287 | 138.57 |
| Gabriele Taentzer | 5 | 2667 | 196.98 |
| Jennifer Plöger | 6 | 4 | 0.38 |