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Learning to Verify Safety Properties

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Title: Learning to Verify Safety Properties
Author(s): Vardhan, Abhay; Sen, Koushik; Viswanathan, Mahesh; Agha, Gul A.
Subject(s): Formal Methods Machine Learning
Abstract: We present a novel approach for verifying safety properties of finite state machines communicating over unbounded FIFO channels that is based on applying machine learning techniques. We assume that we are given a model of the system and learn the set of reachable states from a sample set of executions of the system, instead of attempting to iteratively compute the reachable states. The learnt set of reachable states is then used to either prove that the system is safe or to produce a valid execution of the system leading to an unsafe state (i.e., a counterexample). We have implemented this method for verifying FIFO automata in a tool called LEVER that uses a regular language learning algorithm called RPNI. We apply our tool to a few case studies and report our experience with this method. We also demonstrate how this method can be generalized and applied to the verification of other infinite state systems.
Issue Date: 2004-06
Genre: Technical Report
Type: Text
URI: http://hdl.handle.net/2142/10815
Rights Information: You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS: 2009-04-14
 

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