Iterative Approach to TLC Model Checker Application

Shkarupylo, Vadym; Blinov, I. Yu.; Chemeris, Alexander; Dusheba, V.V.; Alsayaydeh, Jamil Abedalrahim Jamil; Oliinyk, Аndrii

doi:10.1109/khpiweek53812.2021.9570055

Cited by 2 publications

(5 citation statements)

References 20 publications

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“…In the Table 1, it can be seen that the implementation of the TLC method has appeared to be significantly more effective in terms of the related computational expenses, when comparing to the default BFS alternative: from about 1.902 times (for ). It does correspond to the results obtained previously [6,23], and it can be considered as a significantly viable argument in favor of the DFS implementation when the iterative approach to verification takes place [24]. On the contrary, when addressing the spatial properties of the model checking task resolved with a particular TLC implementation, the picture changes drastically -in terms of the ratio between the numbers of transition system states generated (  BFS S and…”

supporting

confidence: 76%

“…Results of previous experimentations have shown, that, depending on the architectural plane of specification, there is a bound, in terms of the number of state variables, when certain TLC implementation is more efficient in terms of the corresponding computational expenses [6,23].…”

Section: Experimentation and Obtained Resultsmentioning

confidence: 99%

“…Under proper circumstances, the alternative DFS implementation of the TLC method provides a significant verification related time costs reduction. By encompassing also the results of previous experimentations (both synthetic and domain related -avionics) [5,6,23,25], these circumstances (factors) have been discovered to be the number of state variables and the architectural plane of formal specification. These concluding thoughts provide the background to further increase the set of case driven scenarios with the TLC model checker implementations utilized, in an attempt to work out and generalize the rules (recommendations) for a particular TLC implementation practical usage, in terms of the related computational and/or spatial expenses decrease.…”

mentioning

confidence: 91%

“…Nowadays, the complexity level of modern program-algorithmic solutions addressing the scenarios taking place in diverse safety-critical domains, e.g., energy scenarios (Finnish nuclear industry [1]), avionics (satellite operational mode management scenarios [2]), safety critical software as a part of railway control systems [3], etc., typically exceeds the limitations of computational and spatial resources required to successfully apply time-proven formal verification techniques and corresponding instruments in one-to-one manner -due to the exponential growth of transition system state space to be traversed through during an automated formal verification by way of model checking [4]. For instance, considering the avionics scenarios, an exponential growth of both spatial [5] and computational [6] expenses has been faced. To diminish named effect, different approaches have been proposed previously.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Case Driven TLC Model Checker Analysis in Energy Scenario

Shkarupylo,

Blinov,

Dusheba

et al. 2023

Computer Modeling and Intelligent Systems

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Today, model checking techniques and corresponding tools are widely applied in diverse case driven scenarios, the safety critical ones in particular. Addressing current situation in Ukraine, an energy domain is among the topical spheres, where safety critical business processes take place. To foster the functional safety of corresponding program-algorithmic solutions, the model checking techniques and related tools are applied to the formal specifications of named solutions. Doing so is not a trivial task: it depends on a particular use case scenario determining the architecture (structure and couplings) of the resulting design artifact. Moreover, the outcomes of formal techniques and tools application directly depend on specification atomicity level chosen -as a tradeoff between the complexity of programalgorithmic constituent addressed to be represented in formal specification and available computational and spatial resources of the computing platform with model checking technique implementation -because of an exponential growth of transition system state space. To this end, to foster the effectiveness of model checking technique application, with respect to a particular case driven scenario, the analysis of broadly applied TLC model checker has been conducted on the basis of a role model from energy domain. Experimentation has been conducted by addressing two alternative implementations of the TLC method. Both -computational and spatial properties -have been covered. To estimate also the domain related spatial expenses on verification, with respect to the number of software threads utilized, the approximation task has been resolved.

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supporting

confidence: 76%

Section: Experimentation and Obtained Resultsmentioning

confidence: 99%

mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Case Driven TLC Model Checker Analysis in Energy Scenario

Shkarupylo,

Blinov,

Dusheba

et al. 2023

Computer Modeling and Intelligent Systems

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“…Because of this, an ensemble model is proposed, based on the simultaneous division of the search space into subgroups and the evolution of models for such subgroups. As a result, the models were able to identify more specific dependencies and relationships between signals [20][21][22].…”

Section: Proposed Methodsmentioning

confidence: 99%

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

Leoshchenko,

Oliinyk,

Subbotin

et al. 2023

Computer Modeling and Intelligent Systems

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This paper is devoted to the problem of detecting and classifying anomalies for time series data. Some of the important applications of time series anomaly detection are healthcare, fraud detection, and system failure recognition. Despite scientists extensive experience in detecting anomalies in time series, most methods look for individual objects that differ from ordinary objects, but do not take into account the specifics of the data sequence [1]. In this paper, a method for detecting anomalies and clastering time series based on neuroevolutionary approaches is proposed. Prediction-based methods are used to detect anomalies: statistical and deep neural networks are used. Classical clustering methods that accept statistical parameters of series were used for clustering.

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Iterative Approach to TLC Model Checker Application

Cited by 2 publications

References 20 publications

Case Driven TLC Model Checker Analysis in Energy Scenario

Case Driven TLC Model Checker Analysis in Energy Scenario

Neuroevolution Methods for Organizing the Search for Anomalies in Time Series

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