Learning assumptions for compositional verification of timed systems

• Main Authors: LIN, Shang-Wei Lin, LIU, Yang, SUN, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Automatic assume-guarantee reasoning; model checking; timed systems; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4982; https://ink.library.smu.edu.sg/context/sis_research/article/5985/viewcontent/learning.pdf
• Institution: Singapore Management University

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.