Name
Abstract | ||
|---|---|---|
We propose a novel framework of program and invariant synthesis called neural network-guided synthesis. We first show that, by suitably designing and training neural networks, we can extract logical formulas over integers from the weights and biases of the trained neural networks. Based on the idea, we have implemented a tool to synthesize formulas from positive/negative examples and implication constraints, and obtained promising experimental results. We also discuss an application of our method for improving the qualifier discovery in the framework of ICE-learning-based CHC solving, which can in turn be applied to program verification and inductive invariant synthesis. Another potential application is to a neural-network-guided variation of Solar-Lezama's program synthesis by sketching. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-88806-0_12 | STATIC ANALYSIS, SAS 2021 |
DocType | Volume | ISSN |
Conference | 12913 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoki Kobayashi | 1 | 0 | 3.38 |
| Taro Sekiyama | 2 | 0 | 0.34 |
| Issei Sato | 3 | 0 | 0.34 |
| Hiroshi Unno | 4 | 167 | 11.54 |