UML metamodelling and ERP software solutions: experiments with Microsoft DSL tools

Dissertação de mestrado em Sistemas de InformaçãoMicrosoft DSL (Domain-Specific Language) Tools allow the definition at the metamodelling level of graphical languages suited to a particular domain. The DSL Tools also allow the conception of models with those graphical languages. The proof of concept reported in this dissertation focuses on the domain of a part of the Primavera ERP (Enterprise Resource Planning) software solution. It exposes a metamodelling approach which can be followed when using the tool to model visual domain-specific languages. It includes a stereotyping approach, abstract and concrete syntaxes’ setting down. The stereotypes allow the adaptation of the graphical language to the domain. Together, stereotypes and language definition through a metamodel make up the DSVL (Domain-Specific Visual Language). This dissertation explains how to perform both the abstract syntax design through metamodels resembling UML (Unified Modelling Language) class diagrams and the concrete syntax definition through the mapping between the elements in the abstract syntax and the visual constructs of the DSVL. Having metamodels inspired by UML is a pertinent approach defended in this dissertation. UML is a standard with worldwide impact, therefore, graphical languages inspired by UML can be handled by professionals worldwide to design their applications and communicate their design decisions. We can create UML-based graphical languages with Microsoft DSL Tools in order to be able to reason about the solution to the problem domain of a portion of the Primavera ERP and still be able to communicate with professionals familiarized with UML about our design decisions. A compromise between domain knowledge and cross-domain knowledge is established with a UML-inspired language tailored to a specific domain. In this dissertation, stereotypes and domain-specific concepts tailor the graphical languages to the domain, whereas metamodels determine a UML-based syntax for the graphical languages.As Microsoft DSL (Domain-Specific Language) Tools permitem a definição ao nível da metamodelação de linguagens gráficas ajustadas a um domínio em particular. As DSL Tools também permitem a concepção de modelos expressos nessas mesmas linguagens. A prova de conceito reportada nesta dissertação foca-se no domínio de uma parte da solução de software Primavera ERP (Enterprise Resource Planning). A dissertação expõe uma abordagem de metamodelação que pode ser seguida durante a utilização da ferramenta DSL Tools para modelar linguagens visuais específicas de um domínio. Inclui uma abordagem de estereotipagem, bem como a definição de sintaxes abstracta e concreta. Os estereótipos permitem a adaptação da linguagem gráfica ao domínio. Juntos, os estereótipos e a definição da linguagem através de um metamodelo constituem a DSVL (Domain-Specific Visual Language). Esta dissertação explica como executar tanto o design da sintaxe abstracta através de metamodelos que se assemelham a diagramas de classes UML (Unified Modelling Language), como a definição da sintaxe concreta através do mapeamento entre os elementos da sintaxe abstracta e os elementos visuais da DSVL. Construir metamodelos inspirados pela UML é uma abordagem pertinente defendida nesta dissertação. A UML é um standard com impacto mundial, logo, linguagens gráficas inspiradas pela UML podem ser manuseadas por profissionais a nível mundial para desenhar as suas aplicações e comunicar as suas decisões de desenho. Podem ser criadas linguagens gráficas baseadas em UML com as Microsoft DSL Tools com o intuito de tornar possível o raciocínio acerca da solução para o domínio do problema de uma porção do ERP Primavera e mesmo assim ser possível comunicar com profissionais familiarizados com a UML acerca das decisões de desenho tomadas. Um compromisso entre o conhecimento do domínio e o conhecimento que é transversal a vários domínios é estabelecido com uma linguagem inspirada em UML e talhada para um domínio específico. Nesta dissertação, os estereótipos e os conceitos específicos do domínio adaptam as linguagens gráficas ao domínio, enquanto que os metamodelos determinam uma sintaxe baseada em UML para as mesmas linguagens gráficas

Evaluating Knowledge Representation and Reasoning Capabilites of Ontology Specification Languages

The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness of "traditional" ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and "web-based" ontology languages. As a result of this study, we conclude that different needs in KR and reasoning may exist in the building of an ontology-based application, and these needs must be evaluated in order to choose the most suitable ontology language(s)

A Theory of Formal Synthesis via Inductive Learning

Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis