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

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

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

A research roadmap towards achieving scalability in model driven engineering

International audienceAs Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and eciency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity , quality, and maintainability benefits. Achieving scalabil-ity in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for ecient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area

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

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

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)

Towards Generating Maintainable and Comprehensible API Code Examples

One of the most effective resources for learning application programming interfaces (APIs) is code examples. The shortage of such examples can pose a significant learning obstacle for API users. API users desire simple, understandable, self-contained examples that are easy to reuse in their applications. However, writing and maintaining code examples that meet the preferences of API users can be a tedious and repetitive activity for API developers. To address this issue, we present a new approach that aims to ease the writing and maintenance of code examples for API developers, while also improving learnability and comprehension for API users. The approach automatically synthesises linear and more comprehensible API code examples from less repetitive and more maintainable versions by inlining reusable utility methods. We implement this approach in a prototype for the Java programming language. We also evaluate its usefulness in terms of conciseness on a dataset of 600 API code examples extracted from nine open-source Java libraries. The results are encouraging and show that the proposed approach can reduce code repetition and bring a decrease of up to 37% in the lines of code of the evaluated API code examples