Browsing by Subject "Model repositories"

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    An efficient and scalable search engine for models
    (Springer, 2021-12-27) Hernández López, José Antonio; Sánchez Cuadrado, Jesús; Informática y Sistemas
    Search engines extract data from relevant sources and make them available to users via queries. A search engine typically crawls the web to gather data, analyses and indexes it and provides some query mechanism to obtain ranked results. There exist search engines for websites, images, code, etc., but the specific properties required to build a search engine for models have not been explored much. In the previous work, we presented MAR, a search engine for models which has been designed to support a query-by-example mechanism with fast response times and improved precision over simple text search engines. The goal of MAR is to assist developers in the task of finding relevant models. In this paper, we report new developments of MAR which are aimed at making it a useful and stable resource for the community. We present the crawling and analysis architecture with which we have processed about 600,000 models. The indexing process is now incremental and a new index for keyword-based search has been added. We have also added a web user interface intended to facilitate writing queries and exploring the results. Finally, we have evaluated the indexing times, the response time and search precision using different configurations. MAR has currently indexed over 500,000 valid models of different kinds, including Ecore meta-models, BPMN diagrams, UML models and Petri nets. MAR is available at http://mar-search.org.
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    MAR : A structure-based search engine for models.
    (ACM, 2020) Hernández López, José Antonio; Sánchez Cuadrado, Jesús; Informática y Sistemas
    The availability of shared software models provides opportunities for reusing, adapting and learning from them. Public models are typically stored in a variety of locations, including model repositories, regular source code repositories, web pages, etc. To profit from them developers need effective search mechanisms to locate the models relevant for their tasks. However, to date, there has been little success in creating a generic and efficient search engine specially tailored to the modelling domain. In this paper we present MAR, a search engine for models. MAR is generic in the sense that it can index any type of model if its meta-model is known. MAR uses a query-by-example approach, that is, it uses example models as queries. The search takes the model structure into account using the notion of bag of paths, which encodes the structure of a model using paths between model elements and is a representation amenable for indexing. MAR is built over HBase using a specific design to deal with large repositories. Our benchmarks show that the engine is efficient and has fast response times in most cases. We have also evaluated the precision of the search engine by creating model mutants which simulate user queries. A REST API is available to perform queries and an Eclipse plug-in allows end users to connect to the search engine from model editors. We have currently indexed more than 50.000 models of different kinds, including Ecore meta-models, BPMN diagrams and UML models. MAR is available at http://mar-search.org.
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    Morsa: A Scalable Approach for Persisting and Accessing Large Models
    (Springer, 2011) Espinazo Pagán, Javier; Sánchez Cuadrado, Jesús; García Molina, Jesús; Informática y Sistemas
    Applying Model-Driven Engineering (MDE) in industrial-scale systems requires managing complex models which may be very large. These models must be persisted in a scalable way that allows their manipulation by client applications without fully loading them. In this paper we propose Morsa, an approach that provides scalable access to large models through load on demand; model persistence is supported by a NoSQL database. We discuss some load on demand algorithms and database design alternatives. A prototype that integrates transparently with EMF is presented and its evaluation demonstrates that it is capable of fully loading large models with a limited amount of memory. Moreover, a benchmark has been executed, exhibiting better performance than the EMF XMI file-based persistence and the most representative model repository, CDO.