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Defining syntax and providing tool support for agent uml using a textual notation

Abstract: An important role in software engineering is played by design notations. The Agent UML (AUML) notation for sequence diagrams has been widely used to capture the design of interactions between agents. However, AUML is not precisely defined, and there is very little in the way of tool support available. We argue that using a textual notation allows the notation to be precisely defined, and facilitates the development of tool support. We present a textual notation that we have developed, and describe a number of tools that support this notation. One of these tools is a ‘renderer ’ which takes a textual AUML protocol and generates the standard graphical view. The layout of graphical elements in the generated graphical view is done automatically, using a layout algorithm which we present

An agent-oriented approach to change propagation in software evolution

Software maintenance and evolution are inevitable activities since almost all software that is useful and successful stimulates user-generated requests for change and improvements. One of the most critical problems in software maintenance and evolution is to maintain consistency between software artefacts by propagating changes correctly. Although many approaches have been proposed, automated change propagation is still a significant technical challenge in software engineering. In this paper we present a novel, agent-oriented approach to deal with change propagation in evolving software systems that are developed using the Prometheus methodology. A metamodel with a set of the Object Constraint Language (OCL) rules forms the basis of the proposed framework. The underlying change propagation mechanism of our framework is based on the well-known Belief-Desire-Intention (BDI) agent architecture. Traceability information and design heuristics are also incorporated into the framework to facilitate the change propagation process

Patient Information Model to Support Population-level Workload Analysis

Evaluation activities in Design Science Research not only verify utility but also scientific rigour and the truth-like value of prescriptive knowledge contributions. Assuming a lack of guidance, evaluation frameworks like the one of Sonnenberg and vom Brocke have been pro- posed prior to evaluating their actual utility to scholars. This research now aims at evaluating how scholars actually apply the framework in practice and their reasoning for doing so. The research-in-progress paper at hand presents preliminary results from a citation analysis. We find an emphasis on ex-post evaluations in artificial settings and a lack of comprehensive detail on ex-ante evaluation activities. The call to accumulate incremental prescriptive knowledge has mostly been ignored and artifact changes are rarely disclosed. Therefore, we question the missing guidance as the sole reason that scholars emphasise building rather than evaluation. We propose to a conduct case study that investigates the reasons behind scholars’ evaluation decisions

WORKLOAD PREDICTION MODEL OF A PRIMARY HEALTH CENTRE

Managing the growing demand for care due to long-term conditions (LTCs) is a big challenge for primary care providers across the globe. We argue that population-level care for LTC patients registered at a primary health centre (PHC) is possible through workload prediction using care plans. In this paper, we try to answer two research questions: i) How can the future demand for care of the patients with LTCs be predicted? and ii) How is the future demand for care affected by changes? We present a rule-based simulation model that, given the patient details, will predict the number of LTC patients who will be visiting the primary health centre for the next year. Knowing this workload would help the medical practice to meet the upcoming demand for care effectively. Our approach also allows simulation of the effects of changes to practice and resourcing to foresee how these changes may impact the practice. Following the design science research approach, our prediction results have been shared with an expert and the feedback guides us to refine our model