VIP: A Visual Editor and Compiler for v-Promela

Kamel, Moataz; Leue, Stefan

doi:10.1007/3-540-46419-0_32

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…It remains to implement the technique in the latest release of SPIN to take advantage of the improved partial-order reduction (Holzmann, 1999) in that release. Furthermore, the combination of our techniques with vPromela (Leue and Holzmann, 1999;Kamel and Leue, 2000) is also promising; v-Promela is a visual formalism that extends PROMELA with hierarchical modeling and other concepts from object-oriented design. The v-Promela formalism supports the structuring of a model into a hierarchy of capsules, which directly corresponds to our notion of a cluster hierarchy.…”

Section: Resultsmentioning

confidence: 99%

Cluster-Based Partial-Order Reduction

Basten

Bošnački

Geilen

2004

Automated Software Engineering

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Abstract. The verification of concurrent systems through an exhaustive traversal of the state space suffers from the infamous state-space-explosion problem, caused by the many interleavings of actions of different processes in the system. Partial-order reduction is a well-known technique to tackle this problem. In this paper, we present an enhancement of the partial-order-reduction scheme of Holzmann and Peled that uses the hierarchical structure of concurrent systems. Our technique tries to contain dependencies between actions within clusters of processes, capitalizing on the independence of actions in different clusters to reduce the state space to be verified while preserving properties of interest. The paper starts with a formalization of the partial-order-reduction technique and continues with a presentation of our enhanced technique, including a correctness argument. The new technique has been implemented in the verification tool SPIN. We present implementation details, some small experiments, and one larger case study using a cache coherency protocol. The experimental results are encouraging. Compared to standard partial-order reduction, improvements in reductions are obtained from 21% up to 98% in the number of states and 34% up to 99% in the number of state transitions.

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Section: Resultsmentioning

confidence: 99%

Cluster-Based Partial-Order Reduction

Basten

Bošnački

Geilen

2004

Automated Software Engineering

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“…We use the model of a preliminary design of a plain old telephony system (POTS) 3 as an example. This model was generated with the visual modeling tool VIP [10] and contains a number of deadlock problems. It comprises four concurrently executing processes corresponding to two users and two phone handlers.…”

Section: A Motivating Examplementioning

confidence: 99%

Mining Sequential Patterns to Explain Concurrent Counterexamples

Leue

Befrouei

2013

Model Checking Software

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Abstract. Concurrent systems are often modeled using an interleaving semantics. Since system designers tend to think sequentially, it is highly probable that they do not foresee some interleavings that their model encompasses. As a consequence, one of the main sources of failure in concurrent systems is unforeseen interleavings. In this paper, we devise an automated method for revealing unforeseen interleavings in the form of sequences of actions derived from counterexamples obtained by explicit state model checking. In order to extract such sequences we use a data mining technique called sequential pattern mining. Our method is based on contrasting the patterns of a set of counterexamples with the patterns of a set of correct traces that do not violate a desired property. We first argue that mining sequential patterns from the dataset of counterexamples fails due to the inherent complexity of the problem. We then propose a reduction technique designed to reduce the length of the execution traces in order to make the problem more tractable. We finally demonstrate the effectiveness of our approach by applying it to a number of sample case studies.

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“…The Dijkstra dining philosophers problem is implemented in phi16 with 16 philosophers. The Plain Old Telephone Service model is implemented in pots [17].…”

Section: Modelsmentioning

confidence: 99%

Finding safety errors with ACO

Alba

Chicano

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of computational resources if the checked model is large. We propose here the use of a new kind of Ant Colony Optimization (ACO) model, ACOhg, to refute safety properties in concurrent systems. ACO algorithms are stochastic techniques belonging to the class of metaheuristic algorithms and inspired by the foraging behaviour of real ants. The traditional ACO algorithms cannot deal with the model checking problem and thus we use ACOhg to tackle it. The results state that ACOhg algorithms find optimal or near optimal error trails in faulty concurrent systems with a reduced amount of resources, outperforming algorithms that are the state-of-the-art in model checking. This fact makes them suitable for checking safety properties in large concurrent systems, in which traditional techniques fail to find errors because of the model size.

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VIP: A Visual Editor and Compiler for v-Promela

Cited by 14 publications

References 10 publications

Cluster-Based Partial-Order Reduction

Cluster-Based Partial-Order Reduction

Mining Sequential Patterns to Explain Concurrent Counterexamples

Finding safety errors with ACO

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