Abstract
Increasing model-driven engineering use leads to an abundance of models and metamodels in academic and industrial practice. A key technique for the management and maintenance of those artefacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a possibly large amount of data. In this paper we extend the SAMOS framework (Statistical Analysis of MOdelS) to clone detection on Ecore metamodels, using the framework’s n-gram feature extraction, vector space model and clustering capabilities. We perform a case analysis on Ecore metamodels obtained by applying an exhaustive set of single mutations to assess the precision/sensitivity of our technique with respect to various types of mutations. Using mutation analysis, we also briefly evaluate MACH, a comparable UML clone detection tool.
Original language | English |
---|---|
Title of host publication | Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development |
Publisher | SCITEPRESS-Science and Technology Publications, Lda. |
Number of pages | 9 |
ISBN (Print) | 9789897582837 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Externally published | Yes |
Event | Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Madeira, Portugal Duration: 22 Jan 2018 → 24 Jan 2018 |
Conference/symposium
Conference/symposium | Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development |
---|---|
Country/Territory | Portugal |
City | Madeira |
Period | 22/01/18 → 24/01/18 |
Keywords
- Clustering
- Model Clone Detection
- Model-driven Engineering
- R
- Vector Space Model