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.

许多程序与数据库进行交互，并对基础数据架构做出假设。但是，实际上，架构通常需要更改（例如，出于绩效原因）。结果，需要更新基础数据和程序代码，以反映此模式更改。这种财产对开发人员面临重大挑战，被认为是计划演变的繁琐且耗时的方面。该项目旨在通过使用程序合成来简化架构修改过程。该建议的目的是开发合成技术，以帮助开发人员具有不同数据模式的不同方面。特别是，该项目将开发新技术，用于在不同格式之间迁移数据以及更新与修改的模式相互作用的代码的技术。该项目的新颖性是一套新型程序合成技术，可用于将数据自动迁移到所需的格式，并修改任何现有代码以使其与新模式一致。该项目的影响是显着减少架构修改过程中涉及的手动软件工程工作。该项目正在通过机构和正式方法和编程语言会议上的计划和心理研讨会通过研究实习介绍从代表性不足的小组到计算机科学研究的本科生。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来通过评估来进行评估的法定任务。