Collaborative Research: SHF: Medium: Toward Understandability and Interpretability for Neural Language Models of Source Code

人工智能(AI)的进步导致了面向软件开发人员的几种新型工具的开发，这些工具旨在帮助实现软件开发过程中构建和维护软件的各个部分的自动化。然而，复杂的基础深度学习模型和海量训练数据集的结合，使得人们很难解释为什么这些模型以及由它们支持的开发工具会以它们的方式运行。鉴于这些工具开始在软件工程(SE)中发挥越来越重要的作用，必须开发技术，使利益相关者能够更好地理解和使用这些工具，以便能够维护关键的软件基础设施。该项目将开发一个框架和方法，使构建人工智能支持的开发工具的研究人员和使用这些工具的软件工程师都能够解释为什么底层模型做出他们所做的预测。其目的是让研究人员能够详细了解为什么一个模型可能不能像预期的那样运行，从而允许有针对性的改进和知情地创建新模型。这种方法将被整合到人工智能支持的软件开发工具中，使软件工程师能够在知情的情况下决定工具的建议何时可能有用或有害，从而建立对工具使用的信任。可解释性框架还将实现与这些工具的新形式的交互，为随着时间的推移而改进的自然语言反馈提供一种机制。该项目将制作和传播有关建立和使用人工智能编程工具的最佳做法的教育材料。这些材料旨在纳入各级教育中现有的计算机扫盲课程。此外，该项目将专注于从传统上代表性较低的类别中招聘和保留计算机科学专业的学生。该项目有三个具体目标。首先，它将设计一种自动方法，为源代码生成“上下文无关”神经语言模型的行为的全局解释。该项目的这一组成部分将使用因果推理理论将预测从大型语言模型映射到人类可解释的编程语言概念，其中将通过因果干预产生对行为的解释。其次，它将开发自动化技术，通过开发一套可解释性技术来对代码的上下文语言模型进行本地解释，这些技术可生成为给定SE任务定义的行为、基于功能和文本的解释(例如，程序修复)。最后，该项目将创造技术，使研究人员和开发人员能够根据生成的解释向模型提供反馈。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。

Advances in artificial intelligence (AI) have led to the development of several new types of tools for software developers that aim to help automate various parts of the software development process of building and maintaining software. However, the combination of complex underlying deep-learning models and massive training datasets makes it difficult to interpret why these models, and the developer tools powered by them, behave the way they do. Given the increasingly important role that these tools are beginning to play in software engineering (SE), it is imperative that techniques be developed that allow stakeholders to better understand and work with these tools such that critical software infrastructure can be maintained. This project will develop a framework and methodology that enables both researchers who build AI-powered developer tools, and software engineers who use these tools, to interpret why the underlying models make the predictions they do. The objective is to allow researchers to obtain detailed insights into why a model may not be performing as expected, allowing for targeted improvement and informed creation of new models. The methodology will be integrated into AI-powered software development tools, allowing software engineers to make informed decisions about when a tool’s suggestion may be helpful or harmful, thus building trust in their use. The interpretability framework will also enable new forms of interaction with these tools, providing a mechanism for natural language feedback that improves over time. This project will produce and disseminate educational materials on best practices related to building and using AI-powered programming tools. These materials are intended to be integrated into existing computer-literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories.This project has three specific goals. First, it will design an automated approach for generating global explanations of the behavior of “context-free” neural language models for source code. This component of the project will map predictions from large language models to human-interpretable programming language concepts using causal inference theory, wherein explanations of behavior will be generated via causal interventions. Second, it will develop automated techniques for local explanations of contextualized language models of code by developing a set of interpretability techniques that generate behavioral, feature-based, and textual explanations defined for given SE tasks (e.g., program repair). Finally, the project will create techniques that enable researchers and developers to provide feedback to models based on generated explanations.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.