3,936 research outputs found
Confluence Detection for Transformations of Labelled Transition Systems
The development of complex component software systems can be made more
manageable by first creating an abstract model and then incrementally adding
details. Model transformation is an approach to add such details in a
controlled way. In order for model transformation systems to be useful, it is
crucial that they are confluent, i.e. that when applied on a given model, they
will always produce a unique output model, independent of the order in which
rules of the system are applied on the input. In this work, we consider
Labelled Transition Systems (LTSs) to reason about the semantics of models, and
LTS transformation systems to reason about model transformations. In related
work, the problem of confluence detection has been investigated for general
graph structures. We observe, however, that confluence can be detected more
efficiently in special cases where the graphs have particular structural
properties. In this paper, we present a number of observations to detect
confluence of LTS transformation systems, and propose both a new confluence
detection algorithm and a conflict resolution algorithm based on them.Comment: In Proceedings GaM 2015, arXiv:1504.0244
Non-simplifying Graph Rewriting Termination
So far, a very large amount of work in Natural Language Processing (NLP) rely
on trees as the core mathematical structure to represent linguistic
informations (e.g. in Chomsky's work). However, some linguistic phenomena do
not cope properly with trees. In a former paper, we showed the benefit of
encoding linguistic structures by graphs and of using graph rewriting rules to
compute on those structures. Justified by some linguistic considerations, graph
rewriting is characterized by two features: first, there is no node creation
along computations and second, there are non-local edge modifications. Under
these hypotheses, we show that uniform termination is undecidable and that
non-uniform termination is decidable. We describe two termination techniques
based on weights and we give complexity bound on the derivation length for
these rewriting system.Comment: In Proceedings TERMGRAPH 2013, arXiv:1302.599
Automating the transformation-based analysis of visual languages
The final publication is available at Springer via http://dx.doi.org/10.1007/s00165-009-0114-yWe present a novel approach for the automatic generation of model-to-model transformations given a description of the operational semantics of the source language in the form of graph transformation rules. The approach is geared to the generation of transformations from Domain-Specific Visual Languages (DSVLs) into semantic domains with an explicit notion of transition, like for example Petri nets. The generated transformation is expressed in the form of operational triple graph grammar rules that transform the static information (initial model) and the dynamics (source rules and their execution control structure). We illustrate these techniques with a DSVL in the domain of production systems, for which we generate a transformation into Petri nets. We also tackle the description of timing aspects in graph transformation rules, and its analysis through their automatic translation into Time Petri netsWork sponsored by the Spanish Ministry of Science and Innovation, project METEORIC (TIN2008-02081/TIN) and by the Canadian Natural Sciences and Engineering Research Council (NSERC)
Modelling and Analysis Using GROOVE
In this paper we present case studies that describe how the graph transformation tool GROOVE has been used to model problems from a wide variety of domains. These case studies highlight the wide applicability of GROOVE in particular, and of graph transformation in general. They also give concrete templates for using GROOVE in practice. Furthermore, we use the case studies to analyse the main strong and weak points of GROOVE
On the analysis of stochastic timed systems
The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions.
We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators
Checking property preservation of refining transformations for model-driven development
In Model-Driven Software Development, a software product is created through iteratively refined modelling. It is crucial that this process preserves certain desirable properties of the initial model. However, checking this is increasingly difficult as the models are increasingly more refined. We propose an incremental model checking technique to determine the preservation of safety and liveness properties in models of concurrent systems with respect to changes applied on individual processes, formalised as transformations of Labelled Transition Systems. The preservation check involves checking bisimilarity between transformed and new behaviour, and never involves reexploring unchanged behaviour. We prove its correctness and demonstrate its applicability
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