Computational notebooks became indispensable tools for research-related
development, offering unprecedented interactivity and flexibility in the
development process. However, these benefits come at the cost of
reproducibility and an increased potential for bugs. There are many tools for
bug fixing; however, they are generally targeted at the classical linear code.
With the rise of code-fluent Large Language Models, a new stream of smart
bug-fixing tools has emerged. However, the applicability of those tools is
still problematic for non-linear computational notebooks. In this paper, we
propose a potential solution for resolving errors in computational notebooks
via an iterative LLM-based agent. We discuss the questions raised by this
approach and share a novel dataset of computational notebooks containing bugs
to facilitate the research of the proposed approach.

计算笔记本的问题解决方法是使用一个基于迭代的大型语言模型的代理程序，以解决计算笔记本中的错误，并提供了一个包含错误的计算笔记本数据集。

解开绳结：在计算笔记本中利用 LLM 进行错误解决

Untangling Knots: Leveraging LLM for Error Resolution in Computational  Notebooks

Automated program repair has emerged as a powerful technique to mitigate the
impact of software bugs on system reliability and user experience. This paper
introduces RepairAgent, the first work to address the program repair challenge
through an autonomous agent based on a large language model (LLM). Unlike
existing deep learning-based approaches, which prompt a model with a fixed
prompt or in a fixed feedback loop, our work treats the LLM as an agent capable
of autonomously planning and executing actions to fix bugs by invoking suitable
tools. RepairAgent freely interleaves gathering information about the bug,
gathering repair ingredients, and validating fixes, while deciding which tools
to invoke based on the gathered information and feedback from previous fix
attempts. Key contributions that enable RepairAgent include a set of tools that
are useful for program repair, a dynamically updated prompt format that allows
the LLM to interact with these tools, and a finite state machine that guides
the agent in invoking the tools. Our evaluation on the popular Defects4J
dataset demonstrates RepairAgent's effectiveness in autonomously repairing 164
bugs, including 39 bugs not fixed by prior techniques. Interacting with the LLM
imposes an average cost of 270,000 tokens per bug, which, under the current
pricing of OpenAI's GPT-3.5 model, translates to 14 cents of USD per bug. To
the best of our knowledge, this work is the first to present an autonomous,
LLM-based agent for program repair, paving the way for future agent-based
techniques in software engineering.

该论文介绍了 RepairAgent，其是第一个基于大型语言模型（LLM）的自主代理解决程序修复挑战的工作。RepairAgent 通过调用合适的工具来自主规划和执行修复操作，包括收集有关错误的信息、收集修复材料以及验证修复结果，并在先前修复尝试的反馈和信息基础上决定调用哪些工具。该研究表明，RepairAgent 在修复 164 个错误中表现出了良好的效果，并修复了先前技术无法修复的 39 个错误。与 LLM 的交互对每个错误平均造成了 270,000 个 token 的开销，根据 OpenAI GPT-3.5 模型的当前定价，平均每个错误的成本为 14 美分。这项工作是首次提出基于 LLM 的自主代理程序修复技术，为未来软件工程的代理技术铺平了道路。

RepairAgent：基于 LLM 的自主程序修复代理

RepairAgent: An Autonomous, LLM-Based Agent for Program Repair

Initially developed for natural language processing (NLP), Transformers are
now widely used for source code processing, due to the format similarity
between source code and text. In contrast to natural language, source code is
strictly structured, i.e., it follows the syntax of the programming language.
Several recent works develop Transformer modifications for capturing syntactic
information in source code. The drawback of these works is that they do not
compare to each other and consider different tasks. In this work, we conduct a
thorough empirical study of the capabilities of Transformers to utilize
syntactic information in different tasks. We consider three tasks (code
completion, function naming and bug fixing) and re-implement different
syntax-capturing modifications in a unified framework. We show that
Transformers are able to make meaningful predictions based purely on syntactic
information and underline the best practices of taking the syntactic
information into account for improving the performance of the model.

本文研究了如何使用 Transformers 提取和利用源代码中的句法信息，完成代码自动补全、函数命名和 bug 修复等任务，实现了更好的模型性能。