Counterexample-guided abstraction refinement

Clarke, Edmund M.; Grumberg, Orna; Jha, Somesh; Lu, Yuanxu; Veith, Helmut

doi:10.1109/time.2003.1214874

Cited by 309 publications

(450 citation statements)

References 17 publications

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“…Trau [2] is a SMT string solver based on the flattening technique introduced in [1], where flat automata are used to capture simple patterns of common constraints. It relies on a Counter-Example Guided Abstraction Refinement (CEGAR) framework [38] where an under-and an over-approximation module interact to increase the string solving precision. In addition, Trau implements string transduction by reduction to context-free membership constraints.…”

Section: Word-based Scs Approachesmentioning

confidence: 99%

A Novel Approach to String Constraint Solving

Amadini

Gange

Stuckey

et al. 2017

Lecture Notes in Computer Science

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String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the last years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This paper reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the last decades to solve string constraints.

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Section: Word-based Scs Approachesmentioning

confidence: 99%

A Novel Approach to String Constraint Solving

Amadini

Gange

Stuckey

et al. 2017

Lecture Notes in Computer Science

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“…accuracy of ML models and also to gain more assurance in the overall system containing the ML component. A more detailed study of using misclassifications (ML component-level counterexamples) to improve the accuracy of the neural network is presented in [12]; this approach is termed counterexample-guided data augmentation, inspired by counterexample-guided abstraction refinement (CEGAR) [8] and similar paradigms.…”

Section: Semantic Trainingmentioning

confidence: 99%

Semantic Adversarial Deep Learning

Dreossi

Jha

Seshia

2018

Computer Aided Verification

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Abstract.Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences.However, existing approaches to generating adversarial examples and devising robust ML algorithms mostly ignore the semantics and context of the overall system containing the ML component. For example, in an autonomous vehicle using deep learning for perception, not every adversarial example for the neural network might lead to a harmful consequence. Moreover, one may want to prioritize the search for adversarial examples towards those that significantly modify the desired semantics of the overall system. Along the same lines, existing algorithms for constructing robust ML algorithms ignore the specification of the overall system. In this paper, we argue that the semantics and specification of the overall system has a crucial role to play in this line of research. We present preliminary research results that support this claim.

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“…This approach can be viewed as the dual of counterexample-guided abstraction refinement [9]. CEGAR starts from an abstract model that represents an over-approximation of the system dynamics, and uses counterexamples (FPs) to refine the model, thereby reducing the FP rate.…”

Section: Reducing the False Negative Ratementioning

confidence: 99%

Neural State Classification for Hybrid Systems

Phan

Paoletti

Zhang

et al. 2018

Automated Technology for Verification and Analysis

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We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model checking problem as it entails classifying each state s of a hybrid automaton as either positive or negative, depending on whether or not s satisfies a given time-bounded reachability specification. This is an interesting problem in its own right, which NSC solves using machine-learning techniques, Deep Neural Networks in particular. State classifiers produced by NSC tend to be very efficient (run in constant time and space), but may be subject to classification errors. To quantify and mitigate such errors, our approach comprises: i) techniques for certifying, with statistical guarantees, that an NSC classifier meets given accuracy levels; ii) tuning techniques, including a novel technique based on adversarial sampling, that can virtually eliminate false negatives (positive states classified as negative), thereby making the classifier more conservative. We have applied NSC to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to 99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to 0.0015 to 0 after tuning the classifier. We believe that this level of accuracy is acceptable in many practical applications, and that these results demonstrate the promise of the NSC approach. arXiv:1807.09901v1 [cs.LG] 26 Jul 2018We call such a function a state classifier. SCP generalizes the model checking problem. Model checking, in the context of SCP, is simply the problem of determining whether there exists a positive state in the set of initial states. Its intent is not to classify all states in S.Classifying the states of a complex system is an interesting problem in its own right. State classification is also useful in at least two other contexts. First, due to random disturbances, a hybrid system may restart in a random state outside the initial region, and we may wish to check the system's safety from that state. Secondly, a classifier can be used for online model checking [26], where in the process of monitoring a system's behavior, one would like to determine, in real-time, the fate of the system going forward from the current (non-initial) state.This paper shows how deep neural networks (DNNs) can be used for state classification, an approach we refer to as Neural State Classification (NSC). An NSC classifier is subject to false positives (FPs) -a state s is deemed positive when it is actually negative, and, more importantly, false negatives (FNs)s is deemed negative when it is actually positive.A well-trained NSC classifier offers high accuracy, runs in constant time (approximately 1 millisecond, in our experiments), and takes constant space (e.g., a DNN with l hidden layers and n neurons only requires functions of dimension l · n for its encoding). This makes NSC classifiers very appealing for applications such as online model checking, a type of analysis subject to strict time and space constraints...

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Counterexample-guided abstraction refinement

Cited by 309 publications

References 17 publications

A Novel Approach to String Constraint Solving

A Novel Approach to String Constraint Solving

Semantic Adversarial Deep Learning

Neural State Classification for Hybrid Systems

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