Construction and abstraction: contrasting methods of supporting model building in learning science

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Scoping analytical usability evaluation methods: A case study

Analytical usability evaluation methods (UEMs) can complement empirical evaluation of systems: for example, they can often be used earlier in design and can provide accounts of why users might experience difficulties, as well as what those difficulties are. However, their properties and value are only partially understood. One way to improve our understanding is by detailed comparisons using a single interface or system as a target for evaluation, but we need to look deeper than simple problem counts: we need to consider what kinds of accounts each UEM offers, and why. Here, we report on a detailed comparison of eight analytical UEMs. These eight methods were applied to it robotic arm interface, and the findings were systematically compared against video data of the arm ill use. The usability issues that were identified could be grouped into five categories: system design, user misconceptions, conceptual fit between user and system, physical issues, and contextual ones. Other possible categories such as User experience did not emerge in this particular study. With the exception of Heuristic Evaluation, which supported a range of insights, each analytical method was found to focus attention on just one or two categories of issues. Two of the three "home-grown" methods (Evaluating Multimodal Usability and Concept-based Analysis of Surface and Structural Misfits) were found to occupy particular niches in the space, whereas the third (Programmable User Modeling) did not. This approach has identified commonalities and contrasts between methods and provided accounts of why a particular method yielded the insights it did. Rather than considering measures such as problem count or thoroughness, this approach has yielded insights into the scope of each method

Special Session on Industry 4.0

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Inverse software configuration management

Software systems are playing an increasingly important role in almost every aspect of today’s society such that they impact on our businesses, industry, leisure, health and safety. Many of these systems are extremely large and complex and depend upon the correct interaction of many hundreds or even thousands of heterogeneous components. Commensurate with this increased reliance on software is the need for high quality products that meet customer expectations, perform reliably and which can be cost-effectively and safely maintained. Techniques such as software configuration management have proved to be invaluable during the development process to ensure that this is the case. However, there are a very large number of legacy systems which were not developed under controlled conditions, but which still, need to be maintained due to the heavy investment incorporated within them. Such systems are characterised by extremely high program comprehension overheads and the probability that new errors will be introduced during the maintenance process often with serious consequences. To address the issues concerning maintenance of legacy systems this thesis has defined and developed a new process and associated maintenance model, Inverse Software Configuration Management (ISCM). This model centres on a layered approach to the program comprehension process through the definition of a number of software configuration abstractions. This information together with the set of rules for reclaiming the information is stored within an Extensible System Information Base (ESIB) via, die definition of a Programming-in-the- Environment (PITE) language, the Inverse Configuration Description Language (ICDL). In order to assist the application of the ISCM process across a wide range of software applications and system architectures, die PISCES (Proforma Identification Scheme for Configurations of Existing Systems) method has been developed as a series of defined procedures and guidelines. To underpin the method and to offer a user-friendly interface to the process a series of templates, the Proforma Increasing Complexity Series (PICS) has been developed. To enable the useful employment of these techniques on large-scale systems, the subject of automation has been addressed through the development of a flexible meta-CASE environment, the PISCES M4 (MultiMedia Maintenance Manager) system. Of particular interest within this environment is the provision of a multimedia user interface (MUI) to die maintenance process. As a means of evaluating the PISCES method and to provide feedback into die ISCM process a number of practical applications have been modelled. In summary, this research has considered a number of concepts some of which are innovative in themselves, others of which are used in an innovative manner. In combination these concepts may be considered to considerably advance the knowledge and understanding of die comprehension process during the maintenance of legacy software systems. A number of publications have already resulted from the research and several more are in preparation. Additionally a number of areas for further study have been identified some of which are already underway as funded research and development projects

D3.2 Cost Concept Model and Gateway Specification

This document introduces a Framework supporting the implementation of a cost concept model against which current and future cost models for curating digital assets can be benchmarked. The value built into this cost concept model leverages the comprehensive engagement by the 4C project with various user communities and builds upon our understanding of the requirements, drivers, obstacles and objectives that various stakeholder groups have relating to digital curation. Ultimately, this concept model should provide a critical input to the development and refinement of cost models as well as helping to ensure that the curation and preservation solutions and services that will inevitably arise from the commercial sector as ‘supply’ respond to a much better understood ‘demand’ for cost-effective and relevant tools. To meet acknowledged gaps in current provision, a nested model of curation which addresses both costs and benefits is provided. The goal of this task was not to create a single, functionally implementable cost modelling application; but rather to design a model based on common concepts and to develop a generic gateway specification that can be used by future model developers, service and solution providers, and by researchers in follow-up research and development projects.<p></p> The Framework includes:<p></p> • A Cost Concept Model—which defines the core concepts that should be included in curation costs models;<p></p> • An Implementation Guide—for the cost concept model that provides guidance and proposes questions that should be considered when developing new cost models and refining existing cost models;<p></p> • A Gateway Specification Template—which provides standard metadata for each of the core cost concepts and is intended for use by future model developers, model users, and service and solution providers to promote interoperability;<p></p> • A Nested Model for Digital Curation—that visualises the core concepts, demonstrates how they interact and places them into context visually by linking them to A Cost and Benefit Model for Curation.<p></p> This Framework provides guidance for data collection and associated calculations in an operational context but will also provide a critical foundation for more strategic thinking around curation such as the Economic Sustainability Reference Model (ESRM).<p></p> Where appropriate, definitions of terms are provided, recommendations are made, and examples from existing models are used to illustrate the principles of the framework

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration