一句话：提出基于状态加权有限树自动机的框架与算法，在含错误样本的噪声数据上合成正确程序。
论文研究噪声数据下的归纳程序合成问题：给定可能包含错误或损坏的输入-输出样例，仍希望合成出正确程序。作者提出一种新框架及其合成算法，核心是有限树自动机的扩展——状态加权有限树自动机，并说明如何用该框架表述和求解多种噪声数据上的程序合成问题。作者实现了系统，并在 SyGuS 2018 基准套件的问题上进行评估。结果表明，该系统能在噪声数据集下成功合成程序，甚至当数据集中每个输入-输出样例都被污染时，仍可合成出正确程序。
We present a new framework and associated synthesis algorithms for program synthesis over noisy data, i.e., data that may contain incorrect/corrupted input-output examples. This framework is based on an extension of finite tree automata called state-weighted finite tree automata. We show how to apply this framework to formulate and solve a variety of program synthesis problems over noisy data. Results from our implemented system running on problems from the SyGuS 2018 benchmark suite highlight its ability to successfully synthesize programs in the face of noisy data sets, including the ability to synthesize a correct program even when every input-output example in the data set is corrupted.