Comparing learning algorithms in automated assume-guarantee reasoning

Authors:
Yu-Fang Chen;Edmund M. Clarke;Azadeh Farzan;Fei He;Ming-Hsien Tsai;Yih-Kuen Tsay;Bow-Yaw Wang;Lei Zhu
Affiliations:
Academia Sinica, Taiwan;Carnegie Mellon University;University of Toronto, Canada;Tsinghua University, China;National Taiwan University, Taiwan;National Taiwan University, Taiwan;Academia Sinica, Taiwan and Tsinghua University, China and INRIA, France;Tsinghua University, China
Venue:
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
Year:
2010

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Abstract

We compare two learning algorithms for generating contextual assumptions in automated assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual assumptions by a conjunction of DNF formulae, while the OBDD learning algorithm uses ordered binary decision diagrams as its representation. Using these learning algorithms, the performance of assume-guarantee reasoning is compared with monolithic interpolation-based Model Checking in parametrized hardware test cases.