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

合作研究：SHF：媒介：实现源代码神经语言模型的可理解性和可解释性

• 批准号: 2423813
• 负责人: Kevin Moran
• 金额: $ 74.52万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2423813&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Toward

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.

人工智能（AI）的进步已经为软件开发人员开发了几种新型工具，旨在帮助自动化构建和维护软件的软件开发过程的各个部分。然而，复杂的底层深度学习模型和大量训练数据集的结合使得很难解释为什么这些模型以及由它们驱动的开发工具会以这种方式运行。鉴于这些工具在软件工程（SE）中开始发挥越来越重要的作用，开发技术使利益相关者能够更好地理解和使用这些工具，以便维护关键的软件基础设施，这是至关重要的。该项目将开发一个框架和方法，使构建人工智能驱动的开发工具的研究人员和使用这些工具的软件工程师能够解释为什么底层模型会做出预测。其目的是让研究人员能够详细了解为什么模型可能没有按预期运行，从而进行有针对性的改进并创建新模型。该方法将被集成到人工智能驱动的软件开发工具中，使软件工程师能够就工具的建议何时可能是有益的或有害的做出明智的决定，从而建立对其使用的信任。可解释性框架还将实现与这些工具的新形式交互，为自然语言反馈提供一种随时间推移而改进的机制。该项目将制作和传播有关构建和使用人工智能编程工具的最佳实践的教育材料。打算将这些材料纳入各级教育的现有计算机扫盲课程。此外，该项目将侧重于从传统上代表性不足的类别中招聘和留住计算机科学专业的学生。首先，它将设计一种自动化方法，用于为源代码生成“上下文无关”神经语言模型行为的全局解释。该项目的这一部分将使用因果推理理论将大型语言模型的预测映射到人类可解释的编程语言概念，其中行为的解释将通过因果干预生成。其次，它将通过开发一组可解释性技术来开发用于代码的上下文化语言模型的本地解释的自动化技术，这些技术生成为给定SE任务定义的行为、基于特征和文本解释（例如，程序修复）。最后，该项目将创建技术，使研究人员和开发人员能够根据生成的解释为模型提供反馈。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。