This paper proposes and evaluates an efficient approach for loading models stored in a change-based format. The work builds on language-independent change-based persistence (CBP) of models conforming to object-oriented metamodelling architectures such as MOF and EMF, an approach which persists a model’s editing history rather than its current state. We evaluate the performance of the proposed loading approach and assess its impact on saving change-based models. Our results show that the proposed approach significantly improves loading times compared to the baseline CBP loading approach, and has a negligible impact on saving.
CITATION STYLE
Yohannis, A., Rodriguez, H. H., Polack, F., & Kolovos, D. (2018). Towards efficient loading of change-based models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10890 LNCS, pp. 235–250). Springer Verlag. https://doi.org/10.1007/978-3-319-92997-2_15
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