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 {\em 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.

中文翻译：

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噪声数据上的归纳程序综合

我们提出了一个新的框架和相关的合成算法，用于在嘈杂的数据上进行程序合成，即可能包含不正确/损坏的输入输出示例的数据。该框架基于有限树自动机的扩展，称为{\em 加权有限树自动机}。我们展示了如何应用这个框架来制定和解决噪声数据上的各种程序综合问题。我们实施的系统在 SyGuS 2018 基准测试套件的问题上运行的结果突出了它在面对嘈杂的数据集时成功合成程序的能力，包括即使在数据集中的每个输入输出示例都已损坏的情况下也能合成正确的程序.