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

这个项目的研究目标是设计新的基于人工智能的软件模型，这些模型可以从人类行为中学习并被告知。软件工程（SE）研究的许多前沿领域涉及基于人工智能的模型在SE任务中的应用。SE研究中的许多任务依赖于相同的基本基础技术，通常是源代码的神经表示，经过训练可以找到代码中的特征，然后用于各种任务，例如，预测文档的单词或可能包含错误的代码区域。虽然基于循环神经网络的编码器-解码器模型的第一个应用是对神经模型所取代的手工制作的启发式和规则的范式转变，但随后的变化产生的改进较少，尽管越来越复杂。这个项目的愿景是在源代码中实现更像人类的神经模型的突破。它的目标是通过改进支持许多下游任务的代码的神经模型，推进依赖于神经模型的广泛的SE研究任务。研究计划分为三部分：首先，该项目将通过眼动追踪和基于ide的实验来描述人类在不同SE任务中的行为。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进源代码的神经表示，并在各种SE任务中评估这些新的表示。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.