一句话：将数据结构合成从单一集合子集操作扩展到多相关集合与求和、计数、最值等聚合属性。
数据结构合成旨在从高层规约自动生成数据结构实现，已有研究显示其能节省程序员时间并降低缺陷风险。然而，现有技术主要针对单一集合的子集操作，难以处理真实程序中常见的多个相关集合，以及求和、计数、最小值和最大值等聚合属性。本文提出泛化的数据结构合成方法，将合成范围扩展到多相关集合与聚合属性，从而覆盖更广泛的实际需求。摘要未给出具体算法细节与实验评估，但该方向有望提升数据结构合成的实用性，并进一步减少手工实现带来的错误。
Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums.
DOI 原文 ·
@inproceedings{paperbot4514,
title = {Generalized data structure synthesis},
author = {Calvin Loncaric and Michael D. Ernst and Emina Torlak},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180211}
}