Rigorous Specification of Use Cases with the RSL Language

RSL language supports the specification of requirements in a systematic, rigorous and consistent way. RSL includes a large set of constructs to produce requirements specifications at different level of abstraction, different writing styles and different types of requirements (e.g., goals, functional requirements, quality requirements, constraints, user stories, and use cases) and tests. This paper focuses only on the RSL views related with use cases, including those constructs directly relevant to the specification of data-intensive information systems, namely: actors, use cases, data entities, state machines, and their respective relationships. The explanation and discussion is held by an illustrative example that shows how to produce such specifications. RSL offers an innovative approach that improves the way requirements specifications are defined and validated. In spite of other proposals, RSL is the first that integrates a large number of inter-related constructs that can be represented in a consistent and systematic way

Ein Ansatz zur Geschäftsmodellierung in der frühen Schiffsentwurfsphase

This dissertation presents an enterprise modeling approach to support the efficiency and dynamic nature of the ship early design phase. The modeling approach is based on the principle concept of the integration between data and process. This concept is applied by taking into consideration the specifications of the early design stage. Therefore, an information model as well as a process model for the ship in the early design stage are developed. Thereby, predefined activity as well as configuration concepts are applied to represent the early design process and support its integrated nature

Automatic generation of user interfaces from rigorous domain and use case models

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

PAV ontology: provenance, authoring and versioning

Provenance is a critical ingredient for establishing trust of published scientific content. This is true whether we are considering a data set, a computational workflow, a peer-reviewed publication or a simple scientific claim with supportive evidence. Existing vocabularies such as DC Terms and the W3C PROV-O are domain-independent and general-purpose and they allow and encourage for extensions to cover more specific needs. We identify the specific need for identifying or distinguishing between the various roles assumed by agents manipulating digital artifacts, such as author, contributor and curator. We present the Provenance, Authoring and Versioning ontology (PAV): a lightweight ontology for capturing just enough descriptions essential for tracking the provenance, authoring and versioning of web resources. We argue that such descriptions are essential for digital scientific content. PAV distinguishes between contributors, authors and curators of content and creators of representations in addition to the provenance of originating resources that have been accessed, transformed and consumed. We explore five projects (and communities) that have adopted PAV illustrating their usage through concrete examples. Moreover, we present mappings that show how PAV extends the PROV-O ontology to support broader interoperability. The authors strived to keep PAV lightweight and compact by including only those terms that have demonstrated to be pragmatically useful in existing applications, and by recommending terms from existing ontologies when plausible. We analyze and compare PAV with related approaches, namely Provenance Vocabulary, DC Terms and BIBFRAME. We identify similarities and analyze their differences with PAV, outlining strengths and weaknesses of our proposed model. We specify SKOS mappings that align PAV with DC Terms.Comment: 22 pages (incl 5 tables and 19 figures). Submitted to Journal of Biomedical Semantics 2013-04-26 (#1858276535979415). Revised article submitted 2013-08-30. Second revised article submitted 2013-10-06. Accepted 2013-10-07. Author proofs sent 2013-10-09 and 2013-10-16. Published 2013-11-22. Final version 2013-12-06. http://www.jbiomedsem.com/content/4/1/3

Integration of DFDs into a UML - based model-driven engineering approach

The main aim of this article is to discuss how the functional and the object-oriented views can be inter-played to represent the various modeling perspectives of embedded systems.We discuss whether the object-oriented modeling paradigm, the predominant one to develop software at the present time, is also adequate for modeling embedded software and how it can be used with the functional paradigm.More specifically, we present how the main modeling tool of the traditional structured methods, data flow diagrams, can be integrated in an object-oriented development strategy based on the unified modeling language. The rationale behind the approach is that both views are important for modeling purposes in embedded systems environments, and thus a combined and integrated model is not only useful, but also fundamental for developing complex systems. The approach was integrated in amodel-driven engineering process, where tool support for the models used was provided. In addition, model transformations have been specified and implemented to automate the process.We exemplify the approach with an IPv6 router case study.FEDER -Fundação para a Ciência e a Tecnologia(HH-02-383

A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study. In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks. Then, we derive from the identified challenges four rules and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project ``Very efficient deep learning in the IoT" (VEDLIoT) in the form of a case study

Semantics-based Privacy by Design for Internet of Things Applications

As Internet of Things (IoT) technologies become more widespread in everyday life, privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers' questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Prot\'eg\'e HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58\% of privacy-related questions from software engineers

Guidelines for the Specification and Design of Large-Scale Semantic Applications

This paper presents a set of guidelines to help software engineers with the specification and design of large-scale semantic applications by defining new processes for Requirements Engineering and Design for semantic applications. To facilitate its use to software engineers not experts in semantic technologies, several techniques are provided, namely, a characterization of large-scale semantic applications, common use cases that appear when developing this type of application, and a set of architectural patterns that can be used for modelling the architecture of semantic applications. The paper also presents an example of how these guidelines can be used and an evaluation of our contributions using the W3C Semantic Web use cases