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: 2210831
• 负责人: Isil Dillig
• 金额: $ 27.5万
• 依托单位: University of Texas at Austin
• 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=2210831&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。然而，在现实中，模式经常需要改变（例如，出于性能原因）。因此，底层数据和程序代码都需要更新，以反映这种模式变化。这给开发人员带来了巨大的挑战，并且被认为是程序演化的一个繁琐和耗时的方面。这个项目旨在通过使用程序合成来简化模式修改过程。该提案的目标是开发合成技术，以帮助开发人员处理更改数据模式的不同方面。特别是，该项目将开发用于在不同格式之间迁移数据的新技术，以及用于更新与修改后的模式交互的代码的技术。 该项目的新颖之处是一套新颖的程序合成技术，可用于自动将数据迁移到所需的格式，并修改任何现有的代码，使其与新的模式保持一致。该项目的影响是显着减少手动软件工程的工作涉及到模式修改过程。该项目通过在两个机构的研究实习项目以及在正式方法和编程语言会议上的指导研讨会，将来自代表性不足群体的本科生引入计算机科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。