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ZENODO
Software . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2023
License: CC BY
Data sources: Datacite
ZENODO
Software . 2023
License: CC BY
Data sources: Datacite
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Artefact for 'On the Use of Model and Logical Embeddings for Model Checking of Stochastic Systems'

Authors: Das, Susmoy; Sharma, Arpit;

Artefact for 'On the Use of Model and Logical Embeddings for Model Checking of Stochastic Systems'

Abstract

The artifact Automatically converts ACTMC specifications of PEPA to PRISM-compatible SCTMC files using model embeddings. For our case studies, we considered stochastic process descriptions written in PEPA, a popular tool for modeling stochastic systems. PEPA allows the state space generation for the corresponding process description, which we inferred as an ACTMC by inferring the generated state space and the process description. Our current prototypical implementation converts PEPA process specifications of ACTMCs to SCTMCs and generates: 1. Three machine-readable files for the PRISM model checker, and 2. A human-readable PRISM code and The outputs are then run on the PRISM model checker, a popular tool for model-checking SCTMCs. The specifications in ACSL and their embedded CSL counterparts, as mentioned in the paper, are also provided in the repositories of each case study, where the formulas are saved in the required PRISM input format to be verified. In a nutshell, to model check an ACTMC with respect to an ACSL property, model the system in PEPA, generate its state-space, and use our code to transform it into an SCTMC and translate the ACSL formula to a CSL formula using the logic embedding discussed in the paper. The CSL formula can then be verified on the SCTMC in PRISM. Detailed instructions are provided in the 'Readme.txt' file.

Keywords

Markov chain, Verification, Logic, Stochastic Model checking, Embedding, Process algebra

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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