Applying Model-Driven Engineering (MDE) leads to the creation of a large number of metamodels, since MDE recommends an intensive use of models defined by metamodels. Metamodels with similar objectives are then inescapably created. A recurrent issue is thus to turn compatible models conforming to similar metamodels, for example to use them in the same tool. The issue is classically solved developing ad hoc model transformations. In this paper, we propose an approach that automatically detects mappings between two metamodels and uses them to generate an alignment between those metamodels. This alignment needs to be manually checked and can then be used to generate a model transformation. Our approach is built on the Similarity Flooding algorithm used in the fields of schema matching and ontology alignment. Experimental results comparing the effectiveness of the application of various implementations of this approach on real-world metamodels are given.
Softwares are designed to be used a significant amount of time, therefore maintenance represents an important part of their life cycle. It has been estimated that a lot of the time allocated to software maintenance is spent on the program comprehension. Many approaches using the program structure or external documentation have been created to ease the program comprehension. However, another important source of information is still not widely used for this purpose: the identifiers. In this article, we propose an approach, based on Natural Language Processing techniques, that automatically extracts and organizes concepts from software identifiers in a WordNet-like structure: lexical views. Those lexical views give useful insight on an overall software architecture and can be used to improve results of many software engineering tasks. The proposal is validated on a corpus of 24 open source softwares.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.