Collaborative Research: SHF: Medium: Semantics-Aware Neural Models of Code

合作研究：SHF：媒介：代码的语义感知神经模型

基本信息

批准号：
2212559
负责人：
Swarat Chaudhuri
金额：
$ 40万
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：
2022-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212559&HistoricalAwards=false
关键词：
Collaborative Research SHF Medium Semantics

项目摘要

Large neural models trained on massive amounts of code still often produce code of poor quality, with such elementary errors as uninitialized variables, type-incorrect expressions, and loops that never finish. Such errors can be a source of insidious software vulnerabilities. The root cause of these issues is that the models treat programs as syntactic rather than semantic artifacts, both during training and generation. The project's novelty is to couple such models with symbolic, semantics-aware methods for program synthesis developed in the formal-methods community. The project develops a neurosymbolic program-synthesis framework that closely couples deep learning and classical symbolic methods for program synthesis. The research explores new learning algorithms in which neural models of code (specifically, transformers) are exposed to explicit knowledge about program semantics, mechanisms that use transformers to direct specification-directed synthesizers, and combinations of classical synthesis and learned models to construct novel compositions of neurally generated programs. The project's impact are a unified framework for semantics-aware program synthesis, yielding better tools for automatically creating programs. The project develops a cross-institution Research Experiences for Undergraduates (REU) program, with a special focus on recruiting women, Hispanic, and Black students to participate.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.

经过大量代码训练的大型神经模型仍然通常会产生质量较差的代码，其基本错误，例如非初始化的变量，类型不正确的表达式和永不结束的循环。这样的错误可能是隐藏软件漏洞的来源。这些问题的根本原因是，在训练和发电期间，模型将程序视为句法而不是语义文物。该项目的新颖性是将此类模型与在正式方法社区中开发的象征性的，语义感知的方法合成方法相结合。该项目开发了一个神经成像程序合成框架，该框架紧密融合了深度学习和程序合成的经典符号方法。该研究探讨了新的学习算法，其中，代码的神经模型（具体是变形金刚）被暴露于有关程序语义的明确知识，使用变压器指导指导指导的合成器的机制以及经典合成和学识渊博的模型的组合以构建神经生成程序的新组成。该项目的影响是语义感知程序综合的统一框架，为自动创建程序提供了更好的工具。该项目开发了本科生（REU）计划的跨机构研究经验，特别着眼于招募妇女，西班牙裔和黑人学生参加。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。