4,093 research outputs found
Improving the visualization of alloy instances
Alloy is a lightweight formal specification language, supported by an IDE, which has proven well-suited for reasoning about software design in early development stages. The IDE provides a visualizer that produces graphical representations of analysis results, which is essential for the proper validation of the model. Alloy is a rich language but inherently static, so behavior needs to be explicitly encoded and reasoned about. Even though this is a common scenario, the visualizer presents limitations when dealing with such models. The main contribution of this paper is a principled approach to generate instance visualizations, which improves the current Alloy Visualizer, focusing on the representation of behavior.This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia, within project POCI-01-0145-FEDER-016826
Clafer: Lightweight Modeling of Structure, Behaviour, and Variability
Embedded software is growing fast in size and complexity, leading to intimate
mixture of complex architectures and complex control. Consequently, software
specification requires modeling both structures and behaviour of systems.
Unfortunately, existing languages do not integrate these aspects well, usually
prioritizing one of them. It is common to develop a separate language for each
of these facets. In this paper, we contribute Clafer: a small language that
attempts to tackle this challenge. It combines rich structural modeling with
state of the art behavioural formalisms. We are not aware of any other modeling
language that seamlessly combines these facets common to system and software
modeling. We show how Clafer, in a single unified syntax and semantics, allows
capturing feature models (variability), component models, discrete control
models (automata) and variability encompassing all these aspects. The language
is built on top of first order logic with quantifiers over basic entities (for
modeling structures) combined with linear temporal logic (for modeling
behaviour). On top of this semantic foundation we build a simple but expressive
syntax, enriched with carefully selected syntactic expansions that cover
hierarchical modeling, associations, automata, scenarios, and Dwyer's property
patterns. We evaluate Clafer using a power window case study, and comparing it
against other notations that substantially overlap with its scope (SysML, AADL,
Temporal OCL and Live Sequence Charts), discussing benefits and perils of using
a single notation for the purpose
Experiences on teaching alloy with an automated assessment platform
This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. Alloy4Fun has been used in formal methods graduate courses for two years and for the latest edition we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).We would like to thank Daniel Jackson for the helpful comments and suggestions about the design of Alloy4Fun. This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. The third and forth authors were financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese
funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project POCI-01-0145-FEDER-016826. The second author was also supported by the FCT sabbatical grant with reference SFRH/BSAB/143106/2018
Automatic Test Generation for Space
The European Space Agency (ESA) uses an engine to perform tests in the Ground
Segment infrastructure, specially the Operational Simulator. This engine uses
many different tools to ensure the development of regression testing
infrastructure and these tests perform black-box testing to the C++ simulator
implementation. VST (VisionSpace Technologies) is one of the companies that
provides these services to ESA and they need a tool to infer automatically
tests from the existing C++ code, instead of writing manually scripts to
perform tests. With this motivation in mind, this paper explores automatic
testing approaches and tools in order to propose a system that satisfies VST
needs
A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters
The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is
rooted in the diverse phases and the crystal structures they contain. In the
realm of material informatics, employing machine learning (ML) techniques to
classify phases and crystal structures of HEAs has gained considerable
significance. In this study, we assembled a new collection of 1345 HEAs with
varying compositions to predict phases. Within this collection, there were 705
sets of data that were utilized to predict the crystal structures with the help
of thermodynamics and electronic configuration. Our study introduces a
methodical framework i.e., the Pearson correlation coefficient that helps in
selecting the strongly co-related features to increase the prediction accuracy.
This study employed five distinct boosting algorithms to predict phases and
crystal structures, offering an enhanced guideline for improving the accuracy
of these predictions. Among all these algorithms, XGBoost gives the highest
accuracy of prediction (94.05%) for phases and LightGBM gives the highest
accuracy of prediction of crystal structure of the phases (90.07%). The
quantification of the influence exerted by parameters on the model's accuracy
was conducted and a new approach was made to elucidate the contribution of
individual parameters in the process of phase prediction and crystal structure
prediction
Epitaxial designs for maximizing efficiency in resonant tunnelling diode based terahertz emitters
We discuss the modelling of high current density InGaAs/AlAs/InP resonant tunneling diodes to maximize their efficiency as THz emitters. A figure of merit which contributes to the wall plug efficiency, the intrinsic resonator efficiency, is used for the development of epitaxial designs. With the contribution of key parameters identified, we analyze the limitations of accumulated stress to assess the manufacturability of such designs. Optimal epitaxial designs are revealed, utilizing thin barriers, with a wide and shallow quantum well that satisfies the strained layer epitaxy constraint. We then assess the advantages to epitaxial perfection and electrical characteristics provided by devices with a narrow InAs sub-well inside a lattice-matched InGaAs alloy. These new structures will assist in the realization of the next-generation submillimeter emitters
A model-driven approach to the conceptual modeling of situations : from specification to validation
A modelagem de situações para aplicações sensíveis ao contexto, também
chamadas de aplicações sensíveis a situações, é, por um lado, uma tarefa chave
para o funcionamento adequado dessas aplicações. Por outro lado, essa também é
uma tafera árdua graças à complexidade e à vasta gama de tipos de situações
possíveis. Com o intuito de facilitar a representação desses tipos de situações em
tempo de projeto, foi criada a Linguagem de Modelagem de Situações (Situation
Modeling Language - SML), a qual se baseia parcialmente em ricas teorias
ontológicas de modelagem conceitual, além de fornecer uma plataforma de detecção
de situação em tempo de execução. Apesar do benefício da existência dessa
infraestrutura, a tarefa de definir tipos de situação é ainda não-trivial, podendo
carregar problemas que dificilmente são detectados por modeladores via inspeções
manuais. Esta dissertação tem o propósito de melhorar e facilitar ainda mais a
definição de tipos de situação em SML propondo: (i) uma maior integração da
linguagem com as teorias ontológicas de modelagem conceitual pelo uso da
linguagem OntoUML, visando aumentar a expressividade dos modelos de situação;
e (ii) uma abordagem para validação de tipos de situação usando um método formal,
visando garantir que os modelos criados correspondam à intenção do modelador.
Tanto a integração quanto a validação são implementadas em uma ferramenta para
especificação, verificação e validação de tipos de situação ontologicamente
enriquecidos.The modeling of situation types for context-aware applications, also called situationaware
applications, is, on the one hand, a key task to the proper functioning of those
applications. On the other hand, it is also a hard task given the complexity and the
wide range of possible situation types. Aiming at facilitating the representation of
those types of situations at design-time, the Situation Modeling Language (SML) was
created. This language is based partially on rich ontological theories of conceptual
modeling and is accompanied by a platform for situation-detection at runtime.
Despite the benefits of the availability of this suitable infrastructure, the definition of
situation types, being a non-trivial task, can still pose problems that are hardly
detected by modelers by manual model inspection. This thesis aims at improving and
facilitating the definition of situation types in SML by proposing: (i) the integration
between the language and the ontological theories of conceptual modeling by using
the OntoUML language, with the purpose of increasing the expressivity of situation
type models; and (ii) an approach for the validation of situation type models using a
lightweight formal method, aiming at increasing the correspondence between the
created models’ instances and the modeler’s intentions. Both the integration and the
validation are implemented in a tool for specification, verification and validation of
ontologically-enriched situation types.CAPE
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