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，这些模型可以学习和了解人类的行为。软件工程(SE)研究的许多领域的前沿涉及基于人工智能的模型在软件工程任务中的应用。SE研究中的许多任务依赖于相同的基本支撑技术，通常是源代码的神经表示，经过训练以找到代码中的功能，然后将这些功能用于各种任务，例如，预测文档或可能包含错误的代码区域的单词。虽然基于递归神经网络的编解码器模型的第一次应用是对人工制定的启发式规则和规则的范式转换，但随后的更改带来的改进较少，尽管提高了复杂性。该项目的愿景是在更接近人类的源代码神经模型方面实现突破。其目的是通过改进支撑许多下游任务的代码的神经模型，推动广泛的依赖神经模型的SE研究任务。研究计划有三个方面：首先，该项目将通过眼球跟踪和基于IDE的实验来表征人类在不同SE任务中的行为。其次，该项目将设计预测甚至模拟人类行为的模型。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法，并在各种SE任务中评估这些新的表示法。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。

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.