Conference paper

CHEN Yu-Fang, HSIEH Chiao, LENGÁL Ondřej, LII Tsung-Ju, TSAI Ming-Hsien, WANG Bow-Yaw and WANG Farn. PAC Learning-Based Verification and Model Synthesis. In: Proceedings of the 38th International Conference on Software Engineering. Austin, TX: Association for Computing Machinery, 2016, pp. 714-724. ISBN 978-1-4503-3900-1. Available from:
Publication language:english
Original title:PAC Learning-Based Verification and Model Synthesis
Title (cs):Verifikace a syntéza modelu založené na PAC učení
Proceedings:Proceedings of the 38th International Conference on Software Engineering
Conference:38th International Conference on Software Engineering
Place:Austin, TX, US
Publisher:Association for Computing Machinery
model synthesis, PAC learning, finite automata, program verification
We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning an approximate regular model of the set of feasible paths in a program, and testing whether this model contains
an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of
probably approximately correct (PAC) learning, which uses sampling instead, and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs
the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.
   author = {Yu-Fang Chen and Chiao Hsieh and Ond{\v{r}}ej Leng{\'{a}}l
	and Tsung-Ju Lii and Ming-Hsien Tsai and Bow-Yaw Wang and
	Farn Wang},
   title = {PAC Learning-Based Verification and Model Synthesis},
   pages = {714--724},
   booktitle = {Proceedings of the 38th International Conference on Software
   year = {2016},
   location = {Austin, TX, US},
   publisher = {Association for Computing Machinery},
   ISBN = {978-1-4503-3900-1},
   language = {english},
   url = {}

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