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

在大量代码上训练的大型神经模型仍然经常产生质量较差的代码，带有诸如未初始化的变量、输入错误的表达式和永远不会结束的循环等基本错误。此类错误可能是潜伏的软件漏洞的来源。这些问题的根本原因是，无论是在训练还是生成过程中，模型都将程序视为语法而不是语义构件。该项目的新奇之处在于将这样的模型与在形式方法社区中开发的用于程序合成的符号、语义感知方法结合在一起。该项目开发了一个神经符号程序合成框架，将深度学习和程序合成的经典符号方法紧密结合在一起。这项研究探索了新的学习算法，其中代码的神经模型(特别是转换器)接触到关于程序语义的显性知识，使用转换器来指导规范制导综合器的机制，以及经典综合和学习模型的组合来构建神经生成程序的新组合。该项目的影响是为语义感知的程序合成提供了一个统一的框架，为自动创建程序产生了更好的工具。该项目开发了一个跨机构的本科生研究体验(REU)计划，特别关注招募女性、西班牙裔和黑人学生参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.