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Faculty of Technology, Design and Environment
This paper presents an overview of the usability engineering process for the development of a personalised clinical decision support system for the management of type 1 diabetes. The tool uses artificial intelligence (AI) techniques to provide insulin bolus dose advice and carbohydrate recommendations that adapt to the individual. We describe the role of human factors and user-centred design in the creation of medical systems that must adhere to international standards. We focus specifically on the formative evaluation stage of this process. The preliminary analysis of data shows promising results.
Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored.
In this paper we suggest using a descriptive approach for defect prediction rather than the precise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result.
We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with publicly available datasets from the Promise repository and object-oriented metrics from an Eclipse repository related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocessing techniques.
The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules.
The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.
Fault localization is the activity of precisely indicating the faulty commands in a buggy program. It is known to be a highly costly and tedious process. Automating this process has been the goal of many studies, showing it to be a challenging problem. The coveragespectrum based approaches commonly apply heuristics grounded on the execution of control-flow components to calculate the odds of each program element to be the defective one. The present study aims to investigate another source of fault information by assessinghow data-flow analysis are useful to compute suspiciousness scores; and how the combination of scores from different sources impacts fault localization. We present an approach to calculate the suspiciousnessscore for each program command by using the execution of data-flow components. Then we use an evolutionary algorithm to search sets of weights to combine heuristics from distinct sources of fault data (both control-flow and data-flow as well as a hybrid strategy). The approach was applied in programs with seeded faults and real faults and evaluated by using absolute metrics to asses its efficacy to locate faults. Furthermore, we introduce a new metric to investigate the dependence of tie-break strategies in buildingthe ranking of suspicious commands. Data-flow based methods demonstrate high effectiveness but increase the need for tie-breaks, unlike the evolutionary hybrid method that keeps competitive the effectiveness and depends less on tie-break strategies.
Adequate testing of AI applications is essential to ensure their quality. However, it is often prohibitively difficult to generate realistic test cases or to check software correctness. This paper proposes a new method called datamorphic testing, which consists of three components: a set of seed test cases, a set of datamorphisms for transforming test cases, and a set of metamorphisms for checking test results. With an example of face recognition application, the paper demonstrates how to develop datamorphic test frameworks, and illustrates how to perform testing in various strategies, and validates the approach using an experiment with four real industrial applications of face recognition.
We describe the role of human factors in the development of a personalised
clinical decision support system for type 1 diabetes self-management.
The tool uses artificial intelligence (AI) techniques to provide insulin bolus
dose advice and carbohydrate recommendations that adapt to the individual.
This paper introduces a pragmatic and practical method for requirements modeling. The method is built using the concepts of our goal sketching technique together with techniques from an enterprise architecture modeling language. Our claim is that our method will help project managers who want to establish early control of their projects and will also give managers confidence in the scope of their project. In particular we propose the inclusion of assumptions as first class entities in the ArchiMate enterprise architecture modeling language and an extension of the ArchiMate Motivation Model principle to allow radical as well as normative analyses. We demonstrate the usefulness of this method using a simple university library system as an example.
Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process.
There are a number of mobile applications available to help patients suffering from Type 1 diabetes to manage their condition, but the quality of these applications varies greatly. This paper details the findings from a systematic analysis of these applications on three mobile platforms (Android, iOS, and Blackberry) that was conducted to establish the state of the art in mobile applications for diabetes management. The findings from this analysis will help to inform the future development of more effective mobile applications to help patients suffering from Type 1 diabetes who wish to manage their condition with a mobile application.