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An Approach to Identifying Inconsistencies in Model-based Systems Engineering
Author(s) -
Sebastian J. I. Herzig,
Ahsan Qamar,
Christiaan J. J. Paredis
Publication year - 2014
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2014.03.044
Subject(s) - viewpoints , computer science , rework , abstraction , context (archaeology) , process (computing) , variety (cybernetics) , matching (statistics) , software engineering , data science , artificial intelligence , programming language , art , paleontology , philosophy , statistics , mathematics , epistemology , visual arts , biology , embedded system
A typical way of managing the inherent complexity of contemporary technical systems is to study them from different viewpoints. Such viewpoints are defined by a variety of factors, including the concerns of interest, level of abstraction, observers and context. Views conforming to these viewpoints are typically highly interrelated since the concerns addressed in the different viewpoints overlap semantically. Such overlaps can lead to inconsistencies. The challenge is to identify and resolve – that is, manage – such inconsistencies. This paper introduces an approach to identifying inconsistencies within the context of Model- Based Systems Engineering (MBSE). In current practice, inconsistencies are typically only discovered after long time intervals, e.g., during reviews. This can result in costly rework or even mission failure. Therefore, actively checking for inconsistencies, and doing so in a continuous fashion, can be valuable. We investigate the hypothesis that all models can be represented by graphs and that inconsistencies can be identified by means of pattern matching. We show that this process is equivalent to inferring inconsistencies by means of deductive reasoning. Finally, we present the results of a proof-of-concept implementation

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