Collaborative Research: SHF: Small: RUI: Context-aware Models of Source Code Summarization

本研究项目的目标是解决软件源代码自动文档生成的关键障碍。 程序员通过在源代码中编写指令来创建软件。 源代码通常很难理解，程序员通常必须花费大量时间编写和更新代码的自然语言描述，以作为其他程序员的指南。 但是程序员往往会避免这项任务，导致难以理解的遗留代码、bug、新手程序员的挣扎以及其他问题。 编写这些自然语言描述的过程被称为“源代码摘要”，这个项目旨在自动化这个过程。 该项目的长期目标是自动文档生成将提高软件工程师的生产力，并普遍提高软件质量。该项目针对的两个关键障碍是：1）代码摘要的上下文感知模型，以及2）改进这些模型的优化和评估过程。 对第（1）项的研究集中在基于神经网络的阅读和理解源代码的新算法。 一段源代码的“上下文”包括周围的源代码、依赖项和从属项、程序员通信、错误报告、体系结构文档和许多其他软件工件。 该提案旨在构建新的代码神经模型，包括注意力图神经网络和动态记忆网络。 第（2）项的研究集中在改进用于评估源代码摘要模型的指标，以及用于训练这些模型的优化函数。 该项目包括这些指标和功能的设计，以及评估它们的实验。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

The objective of this research project is to address key barriers towards automatic documentation generation for software source code. Programmers create software by writing instructions in source code. That source code is often very difficult to understand, and programmers often must spend significant time writing and updating natural language descriptions of the code to serve as a guide to other programmers. But programmers tend to avoid this task, leading to difficult-to-understand legacy code, bugs, struggles for novice programmers, and other problems. The process of writing these natural language descriptions is called "source code summarization" and this project aims to automate this process. The long-term goal of the project is that automatic documentation generation will improve productivity for software engineers, and increase the quality of software generally.The two key barriers that this project targets are: 1) context-aware models of code summarization, and 2) improved optimization and evaluation procedures for those models. The research towards item (1) centers on novel neural network-based algorithms for reading and understanding source code. The "context" of a section of source code includes the surrounding source code, dependencies and dependents, programmer communications, bug reports, architecture documentation, and many other software artifacts. This proposal aims to build new neural models of code that include this context such as attentional graph neural networks and dynamic memory networks. The research towards item (2) centers on improving the metrics used to evaluate models of source code summarization, as well as optimization functions used to train these models. This project includes both design of these metrics and functions, and experiments to evaluate them.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.