Model-Driven Engineering relies on collections of models, which are the primary artefacts for software development. To enable knowledge sharing and reuse, models need to be managed within repositories, where they can be retrieved upon users queries. This paper examines two different techniques for indexing and searching model repositories, with a focus on Web development projects encoded in a Domain Specific Language. Keyword-based and content-based search (also known as query-by-example) are contrasted, with respect to the architecture of the system, the processing of models and queries, and the way in which metamodel knowledge can be exploited to improve search. A thorough experimental evaluation is conducted to examine what parameter configurations lead to better accuracy and to offer an insight in what queries are addressed best by each system.

Model Driven Development may attain substantial productivity gains by exploiting a high level of reuse, across the projects of a same organization or public model repositories. For reuse to take place, developers must be able to perform effective searches across vast collections of models, locate model fragments of potential interest, evaluate the usefulness of the retrieved artifacts and eventually incorporate them in their projects. Given the variety of Web modeling languages, from general purpose to domain specific, from computation independent to platform independent, it is important to implement a search framework capable of harnessing the power of models and of flexibly adapting to the syntax and semantics of the modeling language. In this paper, we explore the use of graph-based similarity search as a tool for expressing queries over model repositories, uniformly represented as collections of labeled graphs. We discuss how the search approach can be parametrized and the impact of the parameters on the perceived quality of the search results.

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As the quantity of software artifacts, mainly source code and software models, stored in repositories increases, the need for their efficient search becomes more important. In this paper we propose content-based query (a.k.a query-by-example) approach for searching software model repositories, in order to retrieve significant models or model fragments. The query-by-example search conveys the user need in form of a model or pattern specified in a coarse way. Our approach incorporates analysis and indexing of models using textual information retrieval techniques, which exploit the knowledge of the metamodel the models conform to. This allows us to explore different segmentation granularities on models and different indexing techniques ranging from simple bag of words, to index structures which integrate metamodel information. We detail the proposed theoretical framework, the implementation of the method upon open-source architectures, and we discuss the results of our experiments upon a public dataset of UML models