LTL Model Checking Based on Binary Classification of Machine Learning

Zhu, Weiping; Wu, Huanmei; Deng, Miaolei

doi:10.1109/access.2019.2942762

Cited by 10 publications

(9 citation statements)

References 47 publications

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“…Literature on machine learning in the field of verification is broad as well [16]. However, related work [17], [18], [19] focuses on other use cases than our study. Thus, differences in the domain, formal languages, and verified properties limit comparability to our work.…”

Section: ) Machine Leaningmentioning

confidence: 99%

“…They train classifiers to detect deadlocks on small models and then apply them to large models, using three different scenarios for evaluation. [19] predicts whether Kripke structures satisfy LTL formulas, randomly generating 50 Kripke structures and 200 LTL formulas. [37] predicts whether workflow nets are sound or not, generating 2209 workflows nets with different characteristics.…”

Section: Machine Learning With Verification Datamentioning

confidence: 99%

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Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions

2022

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Verification techniques play an essential role in detecting undesirable behaviors in many applications like spectrum auctions. By verifying an auction design, one can detect the least favorable outcomes, e.g., the lowest revenue of an auctioneer. However, verification may be infeasible in practice, given the vast size of the state space on the one hand and the large number of properties to be verified on the other hand. To overcome this challenge, we leverage machine-learning techniques. In particular, we create a dataset by verifying properties of a spectrum auction first. Second, we use this dataset to analyze and predict outcomes of the auction and characteristics of the verification procedure. To evaluate the usefulness of machine learning in the given scenario, we consider prediction quality and feature importance. In our experiments, we observe that prediction models can capture relationships in our dataset well, though one needs to be careful to obtain a representative and sufficiently large training dataset. While the focus of this article is on a specific verification scenario, our analysis approach is general and can be adapted to other domains. INDEX TERMSFormal verification, Machine learning, Model checking, Spectrum auctions I. INTRODUCTION A. MOTIVATION

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Section: ) Machine Leaningmentioning

confidence: 99%

Section: Machine Learning With Verification Datamentioning

confidence: 99%

Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions

2022

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“…Subsequently, ML trains the data set to predict the results. The related articles based on the conducted search in this paper for machine learning in model-checking are [42] and [43].…”

Section: E: Machine Learningmentioning

confidence: 99%

Handling State Space Explosion in Component-Based Software Verification: A Review

et al. 2021

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Component-based software development (CBSD) is an alternative approach to constructing software systems that offers numerous benefits, particularly in decreasing the complexity of system design. However, deploying components into a system is a challenging and error-prone task. Model-checking is one of the reliable methods to systematically analyze the correctness of a system. Its brute-force checking of the system's state space assists to significantly expand the level of confidence in the system. Nevertheless, model-checking is limited by a critical problem called state space explosion (SSE). To benefit from modelchecking, an appropriate method is required to reduce SSE. In the past two decades, a great number of SSE reduction methods have been proposed containing many similarities, dissimilarities, and unclear concepts in some cases. This research, firstly, plans to present a review of SSE handling methods and classify them based on their similarities, principle, and characteristics. Second, it investigates the methods for handling SSE problem in the verification process of CBSD and provides insight into the potential limitations, underlining the key challenges for future research efforts. INDEX TERMSComponent-based software development, Verification of software components, Modelchecking, State space explosion.

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“…Recently, machine learning (ML) techniques [5,43] have gained great success in dealing with the state space explosion problem. Zhu et al [43] used machine learning algorithms like random forest, decision trees to predict whether a system (Kripke structure) satisfies a specification (LTL formula). Behjati et al [5] proposed a reinforcement learning based approach for on-the-fly model checking of properties expressed with LTL formulae.…”

Section: Introductionmentioning

confidence: 99%

OCTAL: Graph Representation Learning for LTL Model Checking

Mukherjee¹,

Yin²,

Suresh³

et al. 2022

Preprint

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Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from the state space explosion problem that makes them impractical for large scale systems and/or specifications. In this paper, we propose to use graph representation learning (GRL) for solving linear temporal logic (LTL) model checking, where the system and the specification are expressed by a Büchi automaton and an LTL formula respectively. A novel GRL-based framework OCTAL, is designed to learn the representation of the graph-structured system and specification, which reduces the model checking problem to binary classification in the latent space. The empirical experiments show that OCTAL achieves comparable accuracy against canonical SOTA model checkers on three different datasets, with up to 5× overall speedup and above 63× for satisfiability checking alone.Preprint. Under review.

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LTL Model Checking Based on Binary Classification of Machine Learning

Cited by 10 publications

References 47 publications

Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions

Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions

Handling State Space Explosion in Component-Based Software Verification: A Review

OCTAL: Graph Representation Learning for LTL Model Checking

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