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Incremental inconsistency detection with low memory overhead

Abstract : Ensuring models’ consistency is a key concern when using a model-based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size-independent amount of memory. It will therefore scale better to projects using large models and many consistency rules.
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https://hal.archives-ouvertes.fr/hal-00975337
Contributor : Jean-Rémy Falleri <>
Submitted on : Thursday, March 12, 2020 - 3:05:29 PM
Last modification on : Friday, March 13, 2020 - 1:25:53 PM

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Jean-Rémy Falleri, Xavier Blanc, Reda Bendraou, Marcos Aurélio Almeida da Silva, Cédric Teyton. Incremental inconsistency detection with low memory overhead. Software: Practice and Experience, Wiley, 2014, 44 (5), pp.621-641. ⟨10.1002/spe.2171⟩. ⟨hal-00975337⟩

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