LF-checker：并行验证中有界模型检验的机器学习加速（竞争贡献）
LF-checker: Machine Learning Acceleration of Bounded Model Checking for Concurrency Verification (Competition Contribution)
摘要：
我们描述和评估LF检查器，一个基于机器学习的元验证工具。它提取被测程序的多个特征，并用决策树预测有界模型检查器的最佳配置（标志）。我们目前的工作是专门从事并发验证，并采用ESBMC作为后端验证引擎。在本文中，我们证明了LF检查器实现了更好的结果比默认配置的底层验证引擎。
英文摘要：
We describe and evaluate LF-checker, a metaverifier tool based on machine learning. It extracts multiple features of the program under test and predicts the optimal configuration (flags) of a bounded model checker with a decision tree. Our current work is specialised in concurrency verification and employs ESBMC as a back-end verification engine. In the paper, we demonstrate that LF-checker achieves better results than the default configuration of the underlying verification engine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
