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Challenges and directions in formalizing the semantics of modeling languages
Barrett Richard Bryant, Jeffrey G. Gray, Marjan Mernik, Peter Clarke, Robert France, Gabor Karsai, 2011, original scientific article

Abstract: Developing software from models is a growing practice and there exist many model-based tools (e.g., editors, interpreters, debuggers, and simulators) forsupporting model-driven engineering. Even though these tools facilitate theautomation of software engineering tasks and activities, such tools are typically engineered manually. However, many of these tools have a common semantic foundation centered around an underlying modeling language, which would make it possible to automate their development if the modeling language specification were formalized. Even though there has been much work in formalizing programming languages, with many successful tools constructed using such formalisms, there has been little work in formalizing modeling languages for the purpose of automation. This paper discusses possible semantics-based approaches for the formalization of modeling languages and describes how this formalism may be used to automate the construction of modeling tools.
Keywords: model-based tools, modeling language, semantics
Published in DKUM: 06.07.2017; Views: 1273; Downloads: 396
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Segmenting risks in risk management
Borut Jereb, 2009, original scientific article

Abstract: The paper describes a segmentation of risks to make each risk segment more manageable. The proposed approach is primarily intended to improve the confidentiality of risk simulations. The description of the approach is based on a logistics business process system which requires that its input is represented as a process graph. Each process is defined in terms of input and output; input comprises general input as well as risks; output comprises general output as well as impacts. The model takes into consideration internalas well as external input and output. Parameters can be used to define individual processes. Processes include functions that calculate new values of parameters and output on the bases of given input. Based on given tolerance levels for risks, impacts and process parameters, the model determines whether these levels are acceptable. The model assumes that parameters and functions are non-deterministic, i.e. parameters and functions may change in time. Although the approach is described on a very general level, each segment can be further subdivided into subsegments in order to include more characteristics of observed risks.
Keywords: risk, impact, segmentation, risk management, process parameters, logistics, model, simulation tools, non-deterministic
Published in DKUM: 05.06.2012; Views: 2069; Downloads: 53
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