Collaborative Research: SHF: Core: Medium: Program Synthesis for Schema Changes

协作研究：SHF：核心：媒介：模式更改的程序综合

• 批准号: 2210832
• 负责人: Xinyu Wang
• 金额: $ 45万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210832&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Core; Medium

Many programs interact with databases and make assumptions about the underlying data schema. In reality, however, the schema often needs to change (e.g., for performance reasons). As a result, both the underlying data and program code need to be updated to reflect this schema change. This poses significant challenges to developers and is considered to be a tedious and time-consuming aspect of program evolution. This project aims to simplify the schema modification process through the use of program synthesis. The goal of this proposal is to develop synthesis techniques to help developers with different aspects of changing data schemas. In particular, the project will develop new techniques for migrating data between different formats as well as techniques for updating the code that interacts with a modified schema. The project’s novelties are a suite of novel program synthesis techniques that can be used to automatically migrate the data to the desired format and modify any existing code to be consistent with the new schema. The project's impacts are to significantly reduce the manual software engineering effort involved in the schema modification process. The project is introducing undergraduate students from underrepresented groups to computer science research via research internships through programs at both institutions and mentoring workshops at formal methods and programming languages conferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多程序与数据库交互并对底层数据模式做出假设，但实际上，模式经常需要更改（例如，出于性能原因），底层数据和程序代码都需要更新以反映。这给开发人员带来了重大挑战，被认为是程序演化的一个乏味且耗时的方面。该项目旨在通过使用程序综合来简化模式修改过程。合成技术特别是，该项目将开发在不同格式之间迁移数据的新技术以及更新与修改后的模式交互的代码的技术。综合技术可用于自动将数据迁移到所需的格式并修改任何现有代码以与新模式保持一致。该项目的影响是显着减少模式修改过程中涉及的手动软件工程工作。通过两个机构的研究实习以及正式方法和编程语言会议上的指导研讨会，将代表性不足群体的本科生引入计算机科学研究。该奖项是 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

SlabCity: Whole-Query Optimization using Program Synthesis

• DOI: 10.14778/3611479.3611515
• 发表时间: 2023-07
• 期刊: Proc. VLDB Endow.
• 作者: Rui Dong;Jie Liu-;Yuxuan Zhu;Cong Yan;Barzan Mozafari;Xinyu Wang