Example-guided synthesis of relational queries

Aalok Thakkar, Aaditya Naik, Nathaniel Sands, R. Alur, M. Naik, Mukund Raghothaman
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引用次数: 14

Abstract

Program synthesis tasks are commonly specified via input-output examples. Existing enumerative techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We identify a class of synthesis algorithms for programming-by-examples, which we call Example-Guided Synthesis (EGS), that exploits latent structure in the provided examples while generating candidate programs. We present an instance of EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.
以示例为指导的关系查询合成
程序合成任务通常通过输入-输出示例来指定。用于此类任务的现有枚举技术主要由程序语法指导,并且只间接地使用示例。我们确定了一类用于实例编程的合成算法,我们称之为实例引导合成(EGS),它在生成候选程序时利用所提供示例中的潜在结构。我们提出了一个用于综合关系查询的EGS实例,并对来自三个应用领域的86个任务进行了评估:知识发现、程序分析和数据库查询。我们的评估表明,EGS在合成时间、合成程序的质量和证明不可实现性的能力方面优于基于枚举搜索、约束求解和混合技术的最先进的合成器。
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