Suchergebnisse

Current proposals for designing information systems lack the mechanisms to define statistical functions at the conceptual level. Therefore, queries containing these kind of functions are defined once the rest of the system has already been implemented, which requires much effort and expertise. In this sense, the goal of this paper is to show the benefits of extending the Object Constraint Language (OCL) with a predefined set of statistical functions. ; Work supported by the projects: TIN2008-00444, ESPIA (TIN2007-67078) from the Spanish Ministry of Education and Science (MEC), QUASIMODO (PAC08-0157-0668) from the Castilla-La Mancha Ministry of Education and Science (Spain), and DEMETER (GVPRE/2008/063) from the Valencia Government (Spain). Jose-Norberto Mazón and Jesús Pardillo are funded by MEC under FPU grants AP2005-1360 and AP2006-00332, respectively. Jordi Cabot is funded by the 2007 BP-A 00128 grant (Catalan Government).

Preprint de : Loli Burgueño, Robert Clarisó, Jordi Cabot, Sébastien Gerard and Antonio Vallecillo. "Belief Uncertainty in Software Models." In Proc. of the 11th Workshop on Modelling in Software Engineering (MiSE'2019) at ICSE 2019, Montreal, Canada, May 26-27, 2019. ACM ; This paper discusses the representation of Belief Uncertainty in software models. This kind of uncertainty refers to the situation in which the modeler, or any other belief agent, is uncertain about the behavior of the system, or the statements that the model expresses about it. In this work, we propose to assign a degree of belief to model statements (let they be constraints, or any other model expression), which is expressed by a probability (called credence, in statistical terms) that represents a quantification of such a subjective degree of belief. We discuss how it can be represented using current modeling notations, and how to operate with it in order to make informed decisions. ; PGC2018-094905-B-I00 TIN2016-75944-R CEA in the context of the Modelia initiative and the ECSEL RIA 2016 MegaM@Rt2 project by the European Union's Horizon 2020 under grant No 737494. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.