Title: BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice Show Chinese title

Title: BitsAI-Fix：实践中的LLM驱动的自动Lint错误修复方法

Abstract: As enterprise codebases continue to grow in scale and complexity, the volume of lint errors far exceeds engineers' manual remediation capacity, leading to continuous accumulation of technical debt and hindered development efficiency. This paper presents BitsAI-Fix, an automated lint error remediation workflow based on Large Language Models (LLMs), designed to address this critical challenge in industrial-scale environments. BitsAI-Fix employs tree-sitter for context expansion and generates search-and-replace format patches through specially trained LLMs, followed by lint scan re-verification to output final remediation results. Additionally, our approach introduces an innovative progressive reinforcement learning (RL) training strategy that can automatically acquire verifiable training data during the project cold-start phase and continuously iterate the model by collecting online samples through feedback after system deployment. Furthermore, we designed a targeted rule-based reward mechanism that combines format rewards and correctness rewards while penalizing redundant modifications. We also propose a "code diff matching" methodology to continuously track online effectiveness. In production deployment at ByteDance, our solution has supported over 5,000 engineers, resolved more than 12,000 static analysis issues, achieved approximately 85% remediation accuracy, with around 1,000 weekly active adopters. This work demonstrates the practical feasibility of LLM-based code remediation solutions in enterprise environments and serves as a reference for automated code fix in large-scale industrial scenarios. Show Chinese abstract

Abstract: 随着企业代码库的规模和复杂性不断增加，lint 错误的数量远远超过了工程师手动修复的能力，导致技术债务持续积累并阻碍了开发效率。 本文介绍了 BitsAI-Fix，这是一种基于大型语言模型（LLMs）的自动化 lint 错误修复工作流，旨在解决工业规模环境中的这一关键挑战。 BitsAI-Fix 使用 tree-sitter 进行上下文扩展，并通过专门训练的 LLM 生成搜索与替换格式的补丁，随后进行 lint 扫描重新验证以输出最终修复结果。 此外，我们的方法引入了一种创新的渐进式强化学习（RL）训练策略，可以在项目冷启动阶段自动获取可验证的训练数据，并在系统部署后通过反馈收集在线样本不断迭代模型。 此外，我们设计了一种有针对性的基于规则的奖励机制，结合格式奖励和正确性奖励，同时对冗余修改进行惩罚。 我们还提出了一种“代码差异匹配”方法，以持续跟踪在线效果。 在字节跳动的生产环境中部署后，我们的解决方案已支持超过 5,000 名工程师，解决了超过 12,000 个静态分析问题，达到了约 85% 的修复准确率，每周有大约 1,000 名活跃用户。 这项工作展示了基于 LLM 的代码修复解决方案在企业环境中的实际可行性，并为大规模工业场景中的自动化代码修复提供了参考。