Improved Learning for Stochastic Timed Models by State-Merging Algorithms

Mediouni, Braham Lotfi; Nouri, Ayoub; Bozga, Marius; Bensalem, Saddek

doi:10.1007/978-3-319-57288-8_13

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The statemerging algorithms [8,24] are shown to be effective in learning probabilistic finite automata [12], which in turn, can be converted to DTMC [24]. Although we use the Alergia algorithm [12] to explain our approach; in principle it will work with any learning algorithm that uses an FPTA in its learning process (e.g., [25]). Algorithm 1 demonstrates the main steps of Alergia, adapted from [24] and [8].…”

Section: Training On Rare-event Samplesmentioning

confidence: 99%

Accelerated Learning of Predictive Runtime Monitors for Rare Failure

Babaee

Sedwards

2019

Lecture Notes in Computer Science

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Predictive runtime verification estimates the probability of a future event by monitoring the executions of a system. In this paper we use Discrete-Time Markov Chains (DTMC) as predictive models that are trained from many execution samples demonstrating a rare event: an event that occurs with very low probability. More specifically, we propose a method of grammar inference by which a DTMC is learned with far fewer samples than normal sample distribution. We exploit the concept of importance sampling, and use a mixture of samples, generated from the original system distribution and distributions that are suitably modified to produce more failures. Using the likelihood ratios of the various samples, we ensure the final trained model is faithful to the original distribution. In this way we construct accurate predictive models with orders of magnitude fewer samples. We demonstrate the gains of our approach on a file transmission protocol case study from the literature, and highlight future directions.

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Section: Training On Rare-event Samplesmentioning

confidence: 99%

Accelerated Learning of Predictive Runtime Monitors for Rare Failure

Babaee

Sedwards

2019

Lecture Notes in Computer Science

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“…This work is initially inspired by the recent work on adopting machine learning to learn a variety of system models (e.g., DTMC, stationary models and MDPs) for model checking in order to avoid manual model construction [9,[34][35][36][37]. This work is an attempt to empirically study whether such kind of learning approaches are applicable in real-world settings.…”

Section: Related Workmentioning

confidence: 99%

Learning probabilistic models for model checking: an evolutionary approach and an empirical study

Wang

Sun

Yuan

et al. 2018

Int J Softw Tools Technol Transfer

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Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt modelbased system analysis and development techniques. To overcome this problem, researchers have proposed to automatically "learn" models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? Moreover, how well does learning scale to real-world applications? If the answer is negative, what are the potential methods to improve the efficiency of learning? In this work, we first investigate existing algorithms for learning probabilistic models for model checking and propose an evolution-based approach for better controlling the degree of generalization. Then, we present existing approaches to learn abstract models to improve the efficiency of learning for scalability reasons. Lastly, we conduct an empirical study in order to answer the above questions. Our findings include that the effectiveness of learning may sometimes be limited and it is worth investigating how abstraction should be done properly in order to learn abstract models.

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