Toward neural-network-guided program synthesis and verification

DOI10.1007/978-3-030-88806-0_12zbMath1497.68114arXiv2103.09414OpenAlexW3210636085MaRDI QIDQ2145332

Naoki Kobayashi, Taro Sekiyama, Issei Sato, Hiroshi Unno

Publication date: 17 June 2022

Full work available at URL: https://arxiv.org/abs/2103.09414

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Mathematical aspects of software engineering (specification, verification, metrics, requirements, etc.) (68N30)

Uses Software

• Spacer
• Adam
• Reluplex
• nncontroller
• AI2
• CLN2INV

Cites Work

• SMT-based model checking for recursive programs
• Safety verification of deep neural networks
• Reluplex: an efficient SMT solver for verifying deep neural networks
• Horn Clause Solvers for Program Verification
• Learning safe neural network controllers with barrier certificates
• ICE-based refinement type discovery for higher-order functional programs
• Overfitting in synthesis: theory and practice

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