Debugging Ada Tasking Programs

Heimbold, D.; Luckham, David C.

doi:10.1109/ms.1985.230351

Cited by 162 publications

(49 citation statements)

References 5 publications

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“…A gas station system [18] consists of five components: one operator, one queue, one pump, and two customers. Two customers can fill gas at this gas station.…”

Section: Resultsmentioning

confidence: 99%

Learning Assumptions for CompositionalVerification of Timed Systems

Lin

André

Liu

et al. 2014

IIEEE Trans. Software Eng.

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Abstract-Compositional techniques such as assume-guarantee reasoning (AGR) can help to alleviate the state space explosion problem associated with model checking. However, compositional verification is difficult to be automated, especially for timed systems, because constructing appropriate assumptions for AGR usually requires human creativity and experience. To automate compositional verification of timed systems, we propose a compositional verification framework using a learning algorithm for automatic construction of timed assumptions for AGR. We prove the correctness and termination of the proposed learning-based framework, and experimental results show that our method performs significantly better than traditional monolithic timed model checking.

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“…A gas station system [18] consists of five components: one operator, one queue, one pump, and two customers. Two customers can fill gas at this gas station.…”

Section: Resultsmentioning

confidence: 99%

Learning Assumptions for CompositionalVerification of Timed Systems

Lin

André

Liu

et al. 2014

IIEEE Trans. Software Eng.

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“…All our experiments have been conducted with a 2.4GHz Duo CPU Mac laptop with 2GB of RAM. The case studies we consider are the Gas Station [21], the Smoker [26], and the ATM [12]. The classical examples of Reader/Writer and Producer/Consumer are also considered.…”

Section: Resultsmentioning

confidence: 99%

Incremental Invariant Generation for Compositional Design

Bensalem

Legay

Nguyen

et al. 2010

2010 4th IEEE International Symposium on Theoretical Aspects of Software Engineering

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We consider a compositional method for the verification of component-based systems described in a subset of the BIP language encompassing multi-party interactions. The method is based on the use of two kinds of invariants. Component invariants are over-approximations of components' reachability sets. Interaction invariants are constraints on the states of components involved in interactions. In this paper we propose fixed point characterization for computing interaction invariants. We also propose a new technique that takes the incremental design of the system into account. In many situations, the technique will help to avoid redoing all the verification process each time an interaction is added in the design. Our two techniques have been implemented as extension of the D-Finder toolset. The result has been applied to check deadlock-freedom on several case studies. Our experiments show that our new methodology is generally much faster than existing ones.

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“…Our running example is a gas station (adapted from [23]) that allows customers to refuel their cars using one of the available pumps as follows. When a customer arrives with his car at a pump, he asks the operator to activate that pump for a certain amount of fuel for which he pays in advance or after he finished pumping the gas.…”

Section: Running Example and Requirements For Capturing Itmentioning

confidence: 99%

Data and Abstraction for Scenario-Based Modeling with Petri Nets

Fahland

Prüfer

2012

Lecture Notes in Computer Science

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Abstract. Scenario-based modeling is an approach for describing behaviors of a distributed system in terms of partial runs, called scenarios. Deriving an operational system from a set of scenarios is the main challenge that is typically addressed by either synthesizing system components or by providing operational semantics. Over the last years, several established scenario-based techniques have been adopted to Petri nets. Their adaptation allows for verifying scenario-based models and for synthesizing individual components from scenarios within one formal technique, by building on Petri net theory. However, current adaptations of scenarios face two limitations: a system modeler (1) cannot abstract from concrete behavior, and (2) cannot explicitly describe data in scenarios. This paper lifts these limitations for scenarios in the style of Live Sequence Charts (LSCs). We extend an existing model for scenarios, that features Petri net-based semantics, verification and synthesis techniques, and close the gap between LSCs and Petri nets further.

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Debugging Ada Tasking Programs

Cited by 162 publications

References 5 publications

Learning Assumptions for CompositionalVerification of Timed Systems

Learning Assumptions for CompositionalVerification of Timed Systems

Incremental Invariant Generation for Compositional Design

Data and Abstraction for Scenario-Based Modeling with Petri Nets

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