Synthesizing smart solving strategy for symbolic execution
| Autor: | Zehua Chen, Weiyu Pan, Ziqi Shuai, Zhenbang Chen, Yufeng Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
Constraint (information theory)
Computer science business.industry Programming language Deep learning 0202 electrical engineering electronic engineering information engineering 020207 software engineering 02 engineering and technology Artificial intelligence Symbolic execution business computer.software_genre computer |
| Zdroj: | ASE |
| DOI: | 10.1145/3324884.3418904 |
| Popis: | Constraint solving is one of the challenges for symbolic execution. Modern SMT solvers allow users to customize the internal solving procedure by solving strategies. In this extended abstract, we report our recent progress in synthesizing a program-specific solving strategy for the symbolic execution of a program. We propose a two-stage procedure for symbolic execution. At the first stage, we synthesize a solving strategy by utilizing deep learning techniques. Then, the strategy will be used in the second stage to improve the performance of constraint solving. The preliminary experimental results indicate the promising of our method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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