Towards Statistical Comparison and Analysis of Models

Model comparison is an important challenge in model-driven engineering, with many application areas such as model versioning and domain model recovery. There are numerous techniques that address this challenge in the literature, ranging from graph-based to linguistic ones. Most of these involve pairwise comparison, which might work, e.g. for model versioning with a small number of models to consider. However, they mostly ignore the case where there is a large number of models to compare, such as in common domain model/metamodel recovery from multiple models. In this paper we present a generic approach for model comparison and analysis as an exploratory first step for model recovery. We propose representing models in vector space model, and applying clustering techniques to compare and analyse a large set of models. We demonstrate our approach on a synthetic dataset of models generated via genetic algorithms

Exploring The Limits Of Domain Model Recovery

International audienceWe are interested in re-engineering families of legacy applications towards using Domain-Specific Languages (DSLs). Is it worth to invest in harvesting domain knowledge from the source code of legacy applications? Reverse engineering domain knowledge from source code is sometimes considered very hard or even impossible. Is it also difficult for "modern legacy systems"? In this paper we select two open-source applications and answer the following research questions: which parts of the domain are implemented by the application, and how much can we manually recover from the source code? To explore these questions, we compare manually recovered domain models to a reference model extracted from domain literature, and measured precision and recall. The recovered models are accurate: they cover a significant part of the reference model and they do not contain much junk. We conclude that domain knowledge is recoverable from "modern legacy" code and therefore domain model recovery can be a valuable component of a domain re-engineering process