OCMODELSWARD 2017 Abstracts

Full Papers
Paper Nr: 1

Implicit Incremental Model Analyses and How They Are Affected by Metamodel Design


Georg Hinkel

Abstract: Models can be used to gain insights on the modeled system through analyses. As soon as the model is changed, analyses have to be recomputed for the results to stay valid. Incremental model analyses may help to save time by only reevaluating those parts of a model analysis that are actually affected by a given model change. Deriving such incremental model analyses implicitly saves development effort and maintains a good understandability of the analysis. However, current approaches to implicit incremental model analyses are either restricted to a certain class of analysis or are unable to incorporate optimized incrementalizations of often reused functions. In addition, they do not take the model composition hierarchy into account, which could offer additional efficiency. In the proposed PhD project, these problems are tackled by a combination of 1. an approach to integrate optimized incrementalizations of commonly used analysis operators through a formal representation of the incrementalization process in category theory, 2. an approach to simplify dynamic dependency graphs using the model composition hierarchy, 3. an approach to automatically assemble an incrementalization profile optimized for a given scenario and 4. an approach for lock-free parallel change propagation of model updates. These approaches are applied to a range of case studies, including also analyses formulated as model transformations. Furthermore, the PhD project analyzes how the performance of an incremental model analysis is influenced by the metamodel design and whether relevant design criteria are already met by metamodel designers. A new modeling paradigm is proposed to reduce the accidental complexity and therefore improve the performance of incremental model analyses through the usage of Deep Modeling ideas.