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Journal articles
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.
Cited literature [32 references]
https://hal.archives-ouvertes.fr/hal-00975337
Contributor : Jean-Rémy Falleri <>
Submitted on : Thursday, March 12, 2020 - 3:05:29 PM
Last modification on : Thursday, May 6, 2021 - 2:44:02 PM
Long-term archiving on: : Saturday, June 13, 2020 - 3:33:11 PM
Contributor : Jean-Rémy Falleri <>
Submitted on : Thursday, March 12, 2020 - 3:05:29 PM
Last modification on : Thursday, May 6, 2021 - 2:44:02 PM
Long-term archiving on: : Saturday, June 13, 2020 - 3:33:11 PM
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