CAREER: Democratizing Algorithmic Program Synthesis

程序合成是自动生成满足用户意图的程序的过程。程序综合作为计算机科学和人工智能领域最核心的研究问题之一，具有极大的挑战性。近年来，算法程序合成在现实生活中的代码生成方面取得了成功。尽管取得了很有希望的进展，但现代合成器仍然只能由专家掌握。一个重要的问题是，程序合成并没有集成到大多数普通程序员熟悉的传统软件开发过程中。这个项目朝着降低常规程序员的算法程序合成的门槛迈出了重要的一步。关键的见解是，当前合成过程中具有挑战性的步骤可以被视为合成任务的一部分，并被合并到合成算法中。更具体地说，该项目开发了新的综合框架和算法，使可伸缩的、定量的、基于库的程序综合成为可能，而不需要手工制作目标函数、库模型或分解策略。该项目旨在通过将程序合成器转换为更自然、更易访问的工具，使常规程序员的软件开发变得更容易。该项目还有可能极大地扩大人们的基础，使他们能够克服各自领域与方案编制有关的挑战。该项目将为研究生提供宝贵的培训，使新的教学方法和材料适合于与编译器相关、与合成相关和与网络相关的课程，并为更广泛的社区提供新的计算思维评估和教程，以扩大K-12和本科生的参与。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。

Program synthesis is the process of automatically generating programs that meet the user's intent. As one of the most central research problems in computer science and AI, program synthesis is extremely challenging. Recent years have seen success stories of algorithmic program synthesis in real-life code generation. Despite the promising progress, modern-day synthesizers can still only be mastered by experts. One vital problem is that program synthesis has not been integrated to the traditional software development process that most regular programmers are familiar with. This project takes a major step toward lowering the barrier of algorithmic program synthesis for regular programmers. The key insight is that the challenging steps in the current synthesis process can be perceived as part of the synthesis task and incorporated into the synthesis algorithms. More specifically, this project develops new synthesis frameworks and algorithms that enable scalable, quantitative, library-based program synthesis without hand-crafted objective functions, library models or decomposition strategies. This project aims to make software development easier for regular programmers by transforming program synthesizers into more natural and accessible tools. The project also has the potential to significantly broaden the base of people who can overcome the programming-related challenges in their own fields. The project will provide valuable training for graduate students, enable novel pedagogical approaches and material suitable for compiler-related, synthesis-related and networking-related courses, and provide new computational thinking assessments and tutorials for broadening participation of K-12 and undergraduate students for broader community.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.