Model-based tool support for Tactical Data Links: an experience report from the defence domain

The Tactical Data Link (TDL) allows the exchange of information between cooperating platforms as part of an integrated command and control (C2) system. Information exchange is facilitated by adherence to a complex, message-based protocol defined by document-centric standards. In this paper, we report on a recent body of work investigating migration from a document-centric to a model-centric approach within the context of the TDL domain, motivated by a desire to achieve a positive return on investment. The model-centric approach makes use of the Epsilon technology stack and provides a significant improvement to both the level of abstraction and rigour of the network design. It is checkable by a machine and, by virtue of an MDA-like approach to the separation of domains and model transformation between domains, is open to integration with other models to support more complex workflows, such as by providing the results of interoperability analyses in human-readable domain-specific reports conforming to an accepted standard

Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used

OSSMETER: Automated measurement and analysis of open source software

International audienceDeciding whether an open source software (OSS) meets the requiredstandards for adoption in terms of quality, maturity, activity of development anduser support is not a straightforward process. It involves analysing various sourcesof information, including the project’s source code repositories, communicationchannels, and bug tracking systems. OSSMETER extends state-of-the-art techniquesin the field of automated analysis and measurement of open-source software(OSS), and develops a platform that supports decision makers in the processof discovering, comparing, assessing and monitoring the health, quality, impactand activity of opensource software. To achieve this, OSSMETER computestrustworthy quality indicators by performing advanced analysis and integrationof information from diverse sources including the project metadata, source coderepositories, communication channels and bug tracking systems of OSS projects

Incremental execution of model-to-text transformations using property access traces

Automatic generation of textual artefacts (including code, documentation, configuration files, build scripts, etc.) from models in a software development process through the application of model-to-text (M2T) transformation is a common MDE activity. Despite the importance of M2T transformation, contemporary M2T languages lack support for developing transformations that scale with the size of the input model. As MDE is applied to systems of increasing size and complexity, a lack of scalability in M2T transformation languages hinders industrial adoption. In this paper, we propose a form of runtime analysis that can be used to identify the impact of source model changes on generated textual artefacts. The structures produced by this runtime analysis, property access traces, can be used to perform efficient source-incremental transformation: our experiments show an average reduction of 60% in transformation execution time compared to non-incremental (batch) transformation

Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management

As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis