Establishing and Maintaining Semantically Rich Traceability: A Metamodelling Approach

This thesis addresses the problem of model-to-model traceability in Model Driven Engineering (MDE). A MDE process typically involves models ex- pressed in different modelling languages that capture different views of the system under development. To enhance automation, consistency and co- herency, establishing and maintaining semantically rich traceability links between models used throughout the software development lifecycle is of paramount importance. This thesis deals with the various challenges associated with providing traceability support in the context of MDE by defining a domain-specific, model-based traceability approach, which supports the main traceability ac- tivities in a rigorous and semi-automatic manner. To evaluate the validity of the thesis proposition, a reference implementation has been provided. The results obtained from the application of the proposed approach to various case-studies and examples have confirmed the feasibility and benefits of such an approach

Towards flexible parsing of structured textual model representations

Existing parsers for textual model representation formats such as XMI and HUTN are unforgiving and fail upon even the smallest inconsistency between the structure and naming of metamodel elements and the contents of serialised models. In this paper, we demonstrate how a fuzzy parsing approach can transparently and automatically resolve a number of these inconsistencies, and how it can eventually turn XML into a human-readable and editable textual model representation format for particular classes of models

Converting text to structured models of healthcare services

The paper presents a concise method for transforming textual representations of healthcare services, to a structured, semantically unambiguous modelling language. Employing the method can create structured models of the services that can then be analysed either manually or automatically

Crossflow : A framework for distributed mining of software repositories

Large-scale software repository mining typically requires substantial storage and computational resources, and often involves a large number of calls to (rate-limited) APIs such as those of GitHub and StackOverflow. This creates a growing need for distributed execution of repository mining programs to which remote collaborators can contribute computational and storage resources, as well as API quotas (ideally without sharing API access tokens or credentials). In this paper we introduce Crossflow, a novel framework for building distributed repository mining programs. We demonstrate how Crossflow can delegate mining jobs to remote workers and cache their results, and how workers can implement advanced behaviour such as load balancing and rejecting jobs they cannot perform (e.g. due to lack of space, credentials for a specific API)

Structuring Clinical Decision Support rules for drug safety using Natural Language Processing

Drug safety is an important aspect in healthcare, resulting in a number of inadvertent events, which may harm the patients. IT based Clinical Decision Support (CDS), integrated in electronic-prescription or Electronic Health Records (EHR) systems, can provide a means for checking prescriptions for errors. This requires expressing prescription guidelines in a way that can be interpreted by IT systems. The paper uses Natural Language Processing (NLP), to interpret drug guidelines by the UK NICE BNF offered in free text. The employed NLP component, MetaMap, identifies the concepts in the instructions and interprets their semantic meaning. The UMLS semantic types that correspond to these concepts are then processed, in order to understand the concepts that are needed to be implemented in software engineering for a CDS engine

Supporting Custom Quality Models to Analyse and Compare Open-Source Software

textabstractThe analysis and comparison of open source software can be improved by means of quality models supporting the evaluation of the software systems being compared and the final decision about which of them has to be adopted. Since software quality can mean different things in different scenarios, quality models should be flexible in order to accommodate the needs of different users. Over the years several quality models have been proposed. Even though some of them are tool supported, they are not designed to be extended or customized to better accommodate the requirements of specific business contexts. In this paper, instead of having a fixed model, we propose a workflow and a tool chain to support the specification of custom quality models, which can guide the automated analysis of open source software

A Domain-Specific Language for Monitoring ML Model Performance

As machine learning (ML) starts to offer competitive advantages for an increasing number of application domains, many organisations invest in developing ML-enabled products. The development of these products poses unique challenges compared to traditional software engineering projects and requires the collaboration of people from different disciplines. This work focuses on alleviating some of these challenges related to implementing monitoring systems for deployed ML models. To this end, a domain-specific language (DSL) is developed that data scientists can use to declaratively define monitoring workflows. Complementary to the DSL, a runtime component is developed that implements the specified behaviour. This component is designed to be easily integrated with the rest of an organisation's ML platform and extended by software engineers that do not necessarily have experience with model-driven engineering. An evaluation of the proposed system that supports the validity of the approach is also presented

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance

MedSecurance Project: advanced security-for-safety assurance for medical device IoT (IoMT)

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology “accidents”.This work is co-funded by the HORIZON.2.1 - Health Programme of the European Commission, Grant Agreement number: 101095448 - Advanced Security-for-safety Assurance for Medical Device IoT (MEDSECURANCE).Peer ReviewedArticle signat per 29 autors/es: Parisis GALLOS (a), Rance DeLONG (b), Nicholas MATRAGKAS (c), Allan BLANCHARD (c), Chokri MRAIDHA (c), Gregory EPIPHANIOU (d), Carsten MAPLE (d), Konstantinos KATZIS (e), Jaime DELGADO (f), Silvia LLORENTE (f), Pedro MALÓ (g), Bruno ALMEIDA (g), Andreas MENYCHTAS (h), Christos PANAGOPOULOS (h), Ilias MAGLOGIANNIS (h), Petros PAPACHRISTOU (i), Mariana SOARES (j), Paula BREIA (j), Ana Cristina VIDAL (j), Martin RATZ (k), Ross WILLIAMSON (k), Eduard ERWEE (k), Lukasz STASIAK (k), Orfeu FLORES (l), Carla CLEMENTE (l), John MANTAS (a), Patrick WEBER (a), Theodoros N. ARVANITIS (m) and Scott HANSEN (b) // (a) European Federation of Medical Informatics, Switzerland; (b) The Open Group, UK; (c) CEA, List, Université Paris-Saclay, France; (d) University of Warwick, UK; (e) European University Cyprus, Cyprus; (f) Universitat Politecnica de Catalunya, Spain; (g) Unparallel Innovation, Portugal; (h) BioAssist S.A., Greece; (i) HYGEIA Medical Group, Greece; (j) Centro Garcia de Orta, Hospital Garcia de Orta, Portugal; (k) Doccla AB, Sweden; (l) STAB VIDA, Portugal m University of Birmingham, UKObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version