舒适:合成集合数据结构

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI:10.1145/2950290.2986032

Calvin Loncaric

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引用次数: 0

摘要

许多应用程序需要在标准库中找不到的专用数据结构。手工实现新的数据结构既繁琐又容易出错。为了减轻这个困难，我们构建了一个名为Cozy的工具，它使用反例引导归纳合成来合成数据结构。我们通过在四个实际程序中展示其合成实现与手写实现在正确性和性能方面的比较来评估Cozy。Cozy的数据结构与手写实现的性能相匹配，同时避免了人为错误。

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Cozy: synthesizing collection data structures

Many applications require specialized data structures not found in standard libraries. Implementing new data structures by hand is tedious and error-prone. To alleviate this difficulty, we built a tool called Cozy that synthesizes data structures using counter-example guided inductive synthesis. We evaluate Cozy by showing how its synthesized implementations compare to handwritten implementations in terms of correctness and performance across four real-world programs. Cozy's data structures match the performance of the handwritten implementations while avoiding human error.

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来源期刊

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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