The ModelCC Model-Driven Parser Generator

Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.Comment: In Proceedings PROLE 2014, arXiv:1501.0169

iStarJSON : a lightweight data-format for i* models

JSON is one of the most widely used data-interchange format. There is a large number of tools open for modelling with i*. However, none of them provides supporting for JSON. In this paper we propose iStarJSON language, a JSON-based proposal for interchanging i* models. We also, present an open source software that transforms XML-based format models to JSON models that expose a set of web services for mining iStarJSON models.Peer ReviewedPostprint (author's final draft

Automated testing of Hypermedia REST applications

Testimine on oluline osa tarkvaraarenduse elutsüklis ja testidel põhinev arendamine on üks peamistest praktikatest Agile metoodikas. Tihti keskenduvad programmeerijad RESTful rakenduse loomise protsessis äriloogika testimisele ja unustavad kontrollida protokolli, mis teostab REST interaktsioone. Selles kontekstis pakutakse välja tööriist, mis automatiseerib testide genereerimist ja teostab interaktsioone RESTful rakendusega. Tööriist võtab sisendiks kasutuslood, mis on koostatud Gherkini kitsendatud versiooniga. See on domeenispetsiifiline keel käitumispõhiseks arenduseks. Kasutuslood, mis on kirjutatud selles Gherkini variandis, hõlmavad REST rakenduse poolt nõutud interaktsioone sellisel viisil, et neist on võimalik genereerida teste. Veelgi enam, tööriist genereerib samalt kasutusloolt täisfunktsionaalse pseudoteostuse.\n\rProgrammeerijad saavad kasutada neid pseudoteostusi kliendipoole arendamiseks, vajamata REST rakenduse tegelikku teostust. Käesolev töö tutvustab tööriista kasutust ja disainiprintsiipe ning esitab näite selle kasutamisest.Testing is one essential part of the software development lifecycle and Test Driven Development is one of the main practices in agile methodology. During the development of a RESTful web application, developers oftentimes focus only in testing the business logic and neglect testing the protocol implementing REST interactions. In this context, we propose a tool to automate the generation of test cases that exercise the interactions required by a RESTful application. The tool takes as input user stories written in restricted version of Gherkin, a widely use domain specific language for behaviour driven development. User stories written in this variant of Gherkin capture the essence of the interactions required by a REST application in a way that it is possible to derive test cases from them. Moreover, the tool derives fully functional mock implementations from the same input user story. Such mock implementations can be then used by programmers to develop the client side without requiring the actual implementation of the REST application. This document introduces the design principles and implementation of the tool and presents a study case showcasing its use

Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016), University of Essex, UK, 6 page

RESTful Web Services Development with a Model-Driven Engineering Approach

A RESTful web service implementation requires following the constrains inherent to Representational State Transfer (REST) architectural style, which, being a non-trivial task, often leads to solutions that do not fulfill those requirements properly. Model-driven techniques have been proposed to improve the development of complex applications. In model-driven software development, software is not implemented manually based on informal descriptions, but partial or completely generated from formal models derived from metamodels. A model driven approach, materialized in a domain specific language that integrates the OpenAPI specification, an emerging standard for describing REST services, allows developers to use a design first approach in the web service development process, focusing in the definition of resources and their relationships, leaving the repetitive code production process to the automation provided by model-driven engineering techniques. This also allows to shift the creative coding process to the resolution of the complex business rules, instead of the tiresome and error-prone create, read, update, and delete operations. The code generation process covers the web service flow, from the establishment and exposure of the endpoints to the definition of database tables.A implementação de serviços web RESTful requer que as restrições inerentes ao estilo arquitetónico “Representational State Transfer” (REST) sejam cumpridas, o que, sendo usualmente uma tarefa não trivial, geralmente leva a soluções que não atendem a esses requisitos adequadamente. Técnicas orientadas a modelos têm sido propostas para melhorar o desenvolvimento de aplicações complexas. No desenvolvimento de software orientado a modelos, o software não é implementado manualmente com base em descrições informais, mas parcial ou completamente gerado a partir de modelos formais derivados de meta-modelos. Uma abordagem orientada a modelos, materializada através de uma linguagem específica do domínio que integra a especificação OpenAPI, um padrão emergente para descrever serviços REST, permite aos desenvolvedores usar uma primeira abordagem de design no processo de desenvolvimento de serviços da Web, concentrando-se na definição dos recursos e das suas relações, deixando o processo de produção de código repetitivo para a automação fornecida por técnicas de engenharia orientadas a modelos. Isso também permite focar o processo de codificação criativo na resolução e implementação das regras de negócios mais complexas, em vez de nas operações mais repetitivas e propensas a erros: criação, leitura, atualização e remoção de dados. O processo de geração de código abrange o fluxo do serviço web desde o estabelecimento e exposição dos caminhos para os serviços disponíveis até à definição de tabelas de base de dados

From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho