A probabilistic learning approach for counterexample guided abstraction refinement

Authors:
Fei He;Xiaoyu Song;Ming Gu;Jiaguang Sun
Affiliations:
Dept. Computer Science & Technology, Tsinghua University, Beijing, China;Dept. ECE, Portland State University, Oregon;School of Software, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China
Venue:
ATVA'06 Proceedings of the 4th international conference on Automated Technology for Verification and Analysis
Year:
2006

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Abstract

The paper presents a novel probabilistic learning approach to state separation problem which occurs in the counterexample guided abstraction refinement. The method is based on the sample learning technique, evolutionary algorithm and effective probabilistic heuristics. Compared with the previous work by the sampling decision tree learning solver, the proposed method outperforms 2 to 4 orders of magnitude faster and the size of the separation set is 76% smaller on average.