Actively learning to verify safety for fifo automata (2004) [9 citations — 3 self]

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by Abhay Vardhan, Koushik Sen, Mahesh Viswanathan, Gul Agha

In LNCS 3328, Proc. of FSTTCS’04

http://osl.cs.uiuc.edu/docs/lever-active/activeFifo.ps

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@INPROCEEDINGS{Vardhan04activelylearning,
    author = {Abhay Vardhan and Koushik Sen and Mahesh Viswanathan and Gul Agha},
    title = {Actively learning to verify safety for fifo automata},
    booktitle = {In LNCS 3328, Proc. of FSTTCS’04},
    year = {2004},
    pages = {494--505},
    publisher = {Springer}
}

Abstract:

Abstract. We apply machine learning techniques to verify safety properties of finite state machines which communicate over unbounded FIFO channels. Instead of attempting to iteratively compute the reachable states, we use Angluin's L* algorithm to learn these states. The learnt set of reachable states is then used either to prove that the system is safe, or to produce a valid execution of the system that leads to an unsafe state (i.e. to produce a counterexample). Specifically, we assume that we are given a model of the system and we provide a novel procedure which answers both membership and equivalence queries for a representation of the reachable states. We define a new encoding scheme for representing reachable states and their witness execution; this enables the learning algorithm to analyze a larger class of FIFO systems automatically than a naive encoding would allow. We show the upper bounds on the running time and space for our method. We have implemented our approach in Java, and demonstrate its application to a few case studies. 1

Citations

370	Learning regular sets from queries and counterexamples – Angluin - 1987
325	Dynamically discovering likely program invariants to support program evolution – Ernst, Cockrell, et al. - 2001
123	Mining specifications – Ammons, Bodik, et al. - 2002
115	Well-structured transition systems everywhere – Schnoebelen
88	Inferring Regular Languages in Polynomial Updated Time – Oncina - 1992
75	Regular model checking – Bouajjani, Jonsson, et al. - 2000
58	Learning assumptions for compositional verification – Cobleigh, Giannakopoulou, et al.
53	Symbolic reachability analysis of FIFO-channel systems with nonregular sets of configurations – Bouajjani, Habermehl - 1999
34	Adaptive model checking – Groce, Peled, et al.
29	Symbolic Methods for Exploring Infinite State Spaces – Boigelot - 1999
26	Computer Networks 2nd ed – Tanenbaum - 1989
21	Black box checking – Peled, Vardi, et al. - 1999
19	Algorithmic improvements in regular model checking – Abdulla, Jonsson, et al. - 2003
19	Automatic synthesis of specifications from the dynamic observation of reactive programs – Boigelot, Godefroid - 1997
18	Regular model checking using inference of regular languages – Habermehl, Vojnar - 2004
17	Protocol verification with heuristic search – Edelkamp, Lluch-Lafuente, et al. - 2001
13	Transductions and Context-Free-Languages – Berstel - 1979
12	Automated symbolic analysis of reactive systems – Bultan - 1998
12	Well-abstracted transition systems: Application to FIFO automata – Finkel, Iyer, et al.
10	Regular model checking using widening techniques – Touili - 2001
9	Widening arithmetic automata – Bartzis, Bultan - 2004
9	Learning to Verify Safety Properties – Vardhan, Sen, et al. - 2004
5	Learning to verify tool – LEVER - 2004
2	Learning to verify tool. http://osl.cs.uiuc.edu/˜vardhan/lever.html – LEVER - 2004
2	Regular model checking tool. http://www.regulalrmodelchecking.com – Nilsson - 2004

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