Constraint programming can definitely be seen as a model-driven paradigm. The users write programs for modeling problems. These programs are mapped to executable models to calculate the solutions. This paper focuses on efficient model management (definition and transformation). From this point of view, we propose to revisit the design of constraint-programming systems. A model-driven architecture is introduced to map solving-independent constraint models to solving-dependent decision models. Several important questions are examined, such as the need for a visual highlevel modeling language, and the quality of metamodeling techniques to implement the transformations. A main result is the s-COMMA platform that efficiently implements the chain from modeling to solving constraint problems
Essence is another solver-independent language. Its syntax is addressed to users with a background in discrete mathematics, this style makes Essence a specification language rather than a modeling language. The Essence execution platform allows to map specifications into and Minion solver @cite_12 . A model transformation system called Conjure has been developed, but the integration of solver translators is not its scope. Conjure takes as input an Essence specification and transform it to an intermediate OPL-like language called Essence'. Translators from Essence' to solver code are written by hand.
@cite_12
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We present an extensible API for integrating language modeling and realization, describing its design and efficient implementation in the OpenCCG surface realizer. With OpenCCG, language models may be used to select realizations with preferred word orders, promote alignment with a conversational partner, avoid repetitive language use, and increase the speed of the best-first anytime search. The API enables a variety of n-gram models to be easily combined and used in conjunction with appropriate edge pruning strategies. The n-gram models may be of any order, operate in reverse ("right-to-left"), and selectively replace certain words with their semantic classes. Factored language models with generalized backoff may also be employed, over words represented as bundles of factors such as form, pitch accent, stem, part of speech, supertag, and semantic class. We present a method of translating discrete-time Simulink models to Lustre programs. Our method consists of three steps: type inference, clock inference, and hierarchical bottom-up translation. In the process, we explain and formalize the typing and timing mechanisms of Simulink. The method has been implemented in a prototype tool called S2L, which has been used in the context of a European research project to translate two automotive controller models provided by Audi. Basic language processing such as tokenizing, morphological analyzers, lemmatizing, PoS tagging, chunking, etc. is a need for most NL applications such as Machine Translation, Summarization, Dialogue systems, etc. A large part of the effort required to develop such applications is devoted to the adaptation of existing software resources to the platform, programming language, format or API of the nal system. In LREC’02, we presented the object architecture that we are currently using (Carreras and Padr· o, 2002), which enables the quick and easy integration of basic language analyzers in any NLP application. Now we present a suite of analysis tools based on that architecture, which is distributed under Lesser General Public License (LGPL) (Free Software Foundation, 1999). The rst release of the suite will include morphological analyzer and Part-of-Speech tagger for English, Spanish, and Catalan. Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement level predictions. In this paper, we have proposed PCC to optimize the token level based language modeling. Specifically, PCC introduced an intermediate representation (IR) for source code, which puts tokens into groups using lexeme and variable relative order. In this way, PCC is able to handle long token sequences, i.e., group sequences, to suggest a complete statement with the precise synthesizer. Further more, PCC employed a fuzzy matching technique which combined genetic and longest common sub-sequence algorithms to make the prediction more accurate. We have implemented a code completion plugin for Eclipse and evaluated it on open-source Java projects. The results have demonstrated the potential of PCC in generating precise long statement level predictions. In 30 -60 of the cases, it can correctly suggest the complete statement with only six candidates, and 40 -90 of the cases with ten candidates.