Nowadays, large-scale industrial software systems may involve hundreds of developers working on hundreds of different but related models representing parts of the same system specification. Detecting and resolving structural inconsistencies between these models is then critical. In this article we propose to represent models by sequences of elementary construction operations, rather than by the set of model elements they contain. Structural and methodological consistency rules can then be expressed uniformly as logical constraints on such sequences. Our approach is meta-model independent, allowing us to deal with consistency between different models whatever their kind. We have validated our approach by building a Prolog engine that detects violations of structural and methodological constraints specified on UML 2.1 models and requirement models. This engine has been integrated into two contemporary UML-based modelling environments, Eclipse EMF and Rational Software Architect (RSA).
Due to the increasing use of models, and the inevitable model inconsistencies that arise during model-based software development and evolution, model inconsistency detection is gaining more and more attention. Inconsistency checkers typically analyze entire models to detect undesired structures as defined by inconsistency rules. The larger the models become, the more time the inconsistency detection process takes. Taking into account model evolution, one can significantly reduce this time by providing an incremental checker. In this article we propose an incremental inconsistency checker based on the idea of representing models as sequences of primitive construction operations. The impact of these operations on the inconsistency rules can be computed to analyze and reduce the number of rules that need to be re-checked during a model increment.
Abstract. Formal verification of complex systems using high-level Petri Nets faces the so-called state-space explosion problem. In the context of Petri nets generated from a higher level specification, this problem is particularly acute due to the inherent size of the considered models. A solution is to perform a symbolic analysis of the reachability graph, which exploits the symmetry of a model. Well-Formed Nets (WN) are a class of high-level Petri nets, developed specifically to allow automatic construction of a symbolic reachability graph (SRG), that represents equivalence classes of states. This relies on the definition by the modeler of the symmetries of the model, through the definition of "static sub-classes". Since a model is self-contained, these (a)symmetries are actually defined by the model itself. This paper presents an algorithm capable of automatically extracting the symmetries inherent to a model, thus allowing its symbolic study by translating it to WN. The computation starts from the assumption that the model is entirely symmetric, then examines each component of a net to deduce the symmetry break it induces. This translation is transparent to the end-user, and is implemented as a service for the AMI-Net package. It is particularly adapted to models containing large value domains, yielding combinatorial gain in the size of the reachability graph.
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