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Learning-Based Assume-Guarantee Regression Verification

  • Fei HeEmail author
  • Shu Mao
  • Bow-Yaw Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9779)

Abstract

Due to enormous resource consumption, model checking each revision of evolving systems repeatedly is impractical. To reduce cost in checking every revision, contextual assumptions are reused from assume-guarantee reasoning. However, contextual assumptions are not always reusable. We propose a fine-grained learning technique to maximize the reuse of contextual assumptions. Based on fine-grained learning, we develop a regressional assume-guarantee verification approach for evolving systems. We have implemented a prototype of our approach and conducted extensive experiments (with 1018 verification tasks). The results suggest promising outlooks for our incremental technique.

Keywords

Model Check Symbolic Representation Logical Formula Verification Task Proof Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology (TNList)BeijingChina
  2. 2.School of SoftwareTsinghua UniversityBeijingChina
  3. 3.Key Laboratory for Information System Security, Ministry of EducationBeijingChina
  4. 4.Academia SinicaTaipeiTaiwan

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