Formal synthesis of neural Craig interpolant via counterexample guided deep learning-Reference-Cited by-同舟云学术

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Formal synthesis of neural Craig interpolant via counterexample guided deep learning

Published:2023-11 Issue: Volume:163 Page:107298
ISSN:0950-5849
Container-title:Information and Software Technology
language:en
Short-container-title:Information and Software Technology

Author:

Lin Wang,Ding Mi,Lin Kaipeng,Ding Zuohua

Funder

National Natural Science Foundation of China

State Key Laboratory of Synthetical Automation for Process Industries

Key Technology Research and Development Program of Shandong

Publisher

Elsevier BV

Subject

Computer Science Applications,Information Systems,Software

Reference33 articles.

1. A. Cimatti, A. Griggio, B.J. Schaafsma, R. Sebastiani, The mathsat5 smt solver, in: TACAS’13, 2013, pp. 93–107.

2. A. Brillout, D. Kroening, P. Rümmer, T. Wahl, An interpolating sequent calculus for quantifier-free presburger arithmetic, in: IJCAR’10, 2010, pp. 384–399.

3. K.L. McMillan, Interpolants from z3 proofs, in: FMCAD’11, 2011, pp. 19–27.

4. L. Dai, B. Xia, N. Zhan, Generating non-linear interpolants by semidefinite programming, in: CAV’13, 2013, pp. 364–380.

5. T. Gan, B. Xia, B. Xue, N. Zhan, L. Dai, Nonlinear craig interpolant generation, in: CAV’20, 2020, pp. 415–438.

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