McScM: A General Framework for the Verification of Communicating Machines

Heußner, Alexander; Gall, Tristan Le; Sutre, Grégoire

doi:10.1007/978-3-642-28756-5_34

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…At the core of Dynoptic is a counter-example based abstraction refinement loop [7]. Dynoptic uses the McScM model checker [9] to check the CFSM corresponding to the abstract serialization graph against each of the mined invariants. If an invariant is invalid in the abstract serialization graph, then the model is refined to satisfy the invariant -a partition is split to create less abstract model.…”

Section: Dynoptic Overviewmentioning

confidence: 99%

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Inferring networked system models from behavior traces

Beschastnikh

2012

Proceedings of the 2012 ACM Conference on CoNEXT Student Workshop

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Networked systems are often difficult to debug and understand. A common way of gaining insight into system behavior is to inspect execution logs and documentation. Unfortunately, manual log inspection is difficult and documentation is often incomplete and out of sync with the implementation.To provide developers with more insight into networked systems I am working Dynoptic, a tool that infers a concise and accurate system model, in the form of a communicating finite state machine [5] from logs. Developers can use the inferred models to understand behavior, detect anomalies, verify known bugs, diagnose new bugs, and increase their confidence in the correctness of their implementation. Unlike most related work, Dynoptic does not require developerwritten scenarios, specifications, negative execution examples, or other complex input. Dynoptic processes the logs most systems already produce and requires developers only to specify a set of regular expressions for parsing the logs.

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Section: Dynoptic Overviewmentioning

confidence: 99%

“…Dynoptic relies on the McScM model checker [9,8] for checking the validity of mined invariants in a CFSM model. McScM is one of the most advanced verification tools for concurrent systems; building on prior state of the art [1].…”

Section: Related Workmentioning

confidence: 99%

Inferring networked system models from behavior traces

Beschastnikh

2012

Proceedings of the 2012 ACM Conference on CoNEXT Student Workshop

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“…We were unable to compare our prototype with TReX [5] because the tool is not available anymore. So we compared with McScM [19], which uses the same scm format and can solve the coverability problem for LCS, even though this is not its primary goal. McScM has four verification engines.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The most prominent one is TReX [5]. The tools LASH [8] and McScM [19] are primarily designed to verify systems with perfect channels, but they can also be used for LCSs by explicitly permitting message losses. TReX and LASH iteratively compute the forward reachability set using (1) a symbolic representation of channel contents and (2) so-called acceleration techniques to speedup the computation.…”

mentioning

confidence: 99%

Backward coverability with pruning for lossy channel systems

Geffroy

Leroux

Sutre

2017

Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software

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Driven by the concurrency revolution, the study of the coverability problem for Petri nets has regained a lot of interest in the recent years. A promising approach, which was presented in two papers last year, leverages a downward-closed forward invariant to accelerate the classical backward coverability analysis for Petri nets. In this paper, we propose a generalization of this approach to the class of well-structured transition systems (WSTSs), which contains Petri nets. We then apply this generalized approach to lossy channel systems (LCSs), a well-known subclass of WSTSs. We propose three downward-closed forward invariants for LCSs. One of them counts the number of messages in each channel, and the other two keep track of the order of messages. An experimental evaluation demonstrates the benefits of our approach.

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“…CSight relies on the McScM model checker [33,32], which represents a scalability bottleneck. Future work will use the more efficient Spin model checker [34].…”

Section: Related Workmentioning

confidence: 99%

Inferring models of concurrent systems from logs of their behavior with CSight

Beschastnikh

Brun

Ernst

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

145

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Concurrent systems are notoriously difficult to debug and understand. A common way of gaining insight into system behavior is to inspect execution logs and documentation. Unfortunately, manual inspection of logs is an arduous process, and documentation is often incomplete and out of sync with the implementation.To provide developers with more insight into concurrent systems, we developed CSight. CSight mines logs of a system's executions to infer a concise and accurate model of that system's behavior, in the form of a communicating finite state machine (CFSM).Engineers can use the inferred CFSM model to understand complex behavior, detect anomalies, debug, and increase confidence in the correctness of their implementations. CSight's only requirement is that the logged events have vector timestamps. We provide a tool that automatically adds vector timestamps to system logs. Our tool prototypes are available at http://synoptic.googlecode.com/. This paper presents algorithms for inferring CFSM models from traces of concurrent systems, proves them correct, provides an implementation, and evaluates the implementation in two ways: by running it on logs from three different networked systems and via a user study that focused on bug finding. Our evaluation finds that CSight infers accurate models that can help developers find bugs.

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McScM: A General Framework for the Verification of Communicating Machines

Cited by 9 publications

References 9 publications

Inferring networked system models from behavior traces

Inferring networked system models from behavior traces

Backward coverability with pruning for lossy channel systems

Inferring models of concurrent systems from logs of their behavior with CSight

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