Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSE.2013.57
Title: Learning assumptions for compositionalverification of timed systems
Authors: Lin, S.-W. 
Andre, E.
Liu, Y.
Sun, J.
Dong, J.S. 
Keywords: Automatic assume-guarantee reasoning
model checking
timed systems
Issue Date: 2014
Source: Lin, S.-W., Andre, E., Liu, Y., Sun, J., Dong, J.S. (2014). Learning assumptions for compositionalverification of timed systems. IEEE Transactions on Software Engineering 40 (2) : 137-153. ScholarBank@NUS Repository. https://doi.org/10.1109/TSE.2013.57
Abstract: Compositional techniques such as assume-guarantee reasoning (AGR) can help to alleviate the state space explosion problem associated with model checking. However, compositional verification is difficult to be automated, especially for timed systems, because constructing appropriate assumptions for AGR usually requires human creativity and experience. To automate compositional verification of timed systems, we propose a compositional verification framework using a learning algorithm for automatic construction of timed assumptions for AGR. We prove the correctness and termination of the proposed learning-based framework, and experimental results show that our method performs significantly better than traditional monolithic timed model checking. © 2014 IEEE.
Source Title: IEEE Transactions on Software Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/77877
ISSN: 00985589
DOI: 10.1109/TSE.2013.57
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