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|Title:||Learning assumptions for compositionalverification of timed systems||Authors:||Lin, S.-W.
|Keywords:||Automatic assume-guarantee reasoning
|Issue Date:||2014||Citation:||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|
|Appears in Collections:||Staff Publications|
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