Collaborative Research: SHF: Medium: Towards More Human-like AI Models of Source Code

合作研究：SHF：Medium：迈向更人性化的 AI 源代码模型

基本信息

批准号：
2211428
负责人：
Collin McMillan
金额：
$ 86.4万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211428&HistoricalAwards=false
关键词：
Collaborative Research SHF Medium Towards

项目摘要

The research objective of this project is to design novel artificial intelligence-based models of software that learn from and are informed by human behavior. The frontier of many areas of Software Engineering (SE) research involves applications of AI-based models to SE tasks. Many tasks in SE research rely on the same basic underpinning technologies, often a neural representation of source code that is trained to find features in code, which are then used for various tasks e.g., to predict words for a document or areas of code likely to contain a bug. While the first applications of recurrent neural network-based encoder-decoder models were a paradigm shift over the manually-crafted heuristics and rules that the neural models replaced, subsequent changes have yielded less improvement despite increased sophistication.The vision of this project is to achieve a breakthrough in more human-like neural models of source code. Its aim is to advance a broad spectrum of SE research tasks that rely on neural models, by improving the neural models of code that underpin many downstream tasks. The research plan is three-fold: First, the project will characterize human behavior during different SE tasks via eye-tracking and IDE-based experiments. Second, the project will design models that predict or even mimic human behavior. Third, the project will use those models to augment and improve neural representations of source code, and evaluate these new representations in a variety of SE tasks.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.

该项目的研究目标是设计基于人工智能的新型软件模型，这些模型是通过人类行为学习并告知的。软件工程（SE）研究的许多领域的前沿涉及基于AI的模型在SE任务中的应用。 SE研究中的许多任务都依赖相同的基本基础技术，通常是源代码的神经表示，该技术经过培训以查找代码中的功能，然后将其用于各种任务，例如，例如，用于预测可能包含错误的文档或代码区域的单词。虽然基于神经网络的编码器模型的第一个应用是对手动制作的启发式方法和规则的范式转变，而神经模型已更换了，但尽管这一项目的愿景提高了，但随后的变化却降低了改善。该项目的愿景是实现更多人类的人类神经模型源代码的突破性。其目的是通过改善基于许多下游任务的代码神经模型来促进依赖神经模型的广泛研究任务。研究计划是三个方面的：首先，该项目将通过眼睛跟踪和基于IDE的实验在不同的SE任务中表征人类行为。其次，该项目将设计预测甚至模仿人类行为的模型。第三，该项目将使用这些模型来增强和改善源代码的神经表示，并在各种SE任务中评估这些新表示。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和更广泛影响的审查标准的评估来获得支持的。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization

DOI：
10.1145/3643732
发表时间：
2024-02
期刊：
ArXiv
影响因子：
0
作者：
Yifan Zhang;Jiliang Li;Z. Karas;Aakash Bansal;Toby Jia-Jun Li;Collin McMillan;Kevin Leach;Yu Huang
通讯作者：
Yifan Zhang;Jiliang Li;Z. Karas;Aakash Bansal;Toby Jia-Jun Li;Collin McMillan;Kevin Leach;Yu Huang

Function Call Graph Context Encoding for Neural Source Code Summarization