Visualising Larger State Spaces in Pro B

Leuschel, Michaël; Turner, Edd

doi:10.1007/11415787_2

Cited by 24 publications

(14 citation statements)

References 15 publications

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“…When it is possible, the state space can be very large and flow information difficult to extract. Still, the work in [11] provides various algorithms to visualize the state space in a condensed form. The signature merge algorithm from [11] merges all states with the same signature, and as such will produce a picture very similar to the flow graph.…”

Section: Related and Future Workmentioning

confidence: 99%

“…Still, the work in [11] provides various algorithms to visualize the state space in a condensed form. The signature merge algorithm from [11] merges all states with the same signature, and as such will produce a picture very similar to the flow graph. However, the arcs are not labelled by predicates and the construction requires prior traversal of the state space.…”

Section: Related and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Flow Analysis for Event-B

Bendisposto

Leuschel

2011

Fundamental Approaches to Software Engineering

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Abstract. In Event-B a system is developed using refinement. The language is based on a relatively small core; in particular there is only a very small number of substitutions. This results in much simpler proof obligations, that can be handled by automatic tools. However, the downside is that, in case of software development, structural information is not explicitly available but hidden in the chain of refinements. This paper discusses a method to uncover these implicit algorithmic structures and use them in a model checker. Other applications are code generation, model comprehension, and test-case generation.

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Section: Related and Future Workmentioning

confidence: 99%

Section: Related and Future Workmentioning

confidence: 99%

Automatic Flow Analysis for Event-B

Bendisposto

Leuschel

2011

Fundamental Approaches to Software Engineering

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“…Taking a different approach to coping with highly complex node-link diagrams, Leuschel et al presented techniques to reduce the complexity of the underlying state transition graphs by merging nodes [12].…”

Section: Related Workmentioning

confidence: 99%

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

Blaas

Botha

Grundy

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. A smooth graph representation of a labeled biological time-series. Each ring represents a state, and the edges between states visualize the state transitions. This graph uses smooth curves to explicitly visualize third order transitions, so that each curved edge represents a unique sequence of four successive states. The orange node is part of a selection set, and all transitions matching the current selection are highlighted in orange.Abstract-In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.

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“…The ProB tool [13,14] is an animator and a model checker for B specifications. It provides functionalities to display graphical view of automata.…”

Section: Overview Of Probmentioning

confidence: 99%