BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice

2025-08-05

AI Summary

Enterprise-scale codebases suffer from an explosion of static analysis (lint) errors, leading to inefficient manual remediation and accumulating technical debt. To address this, we propose an LLM-based automated lint error repair framework. Our method integrates Tree-sitter–powered syntactic parsing for context-aware patch generation, adopts a search-and-replace–formatted patch output tailored for industrial deployment, and introduces a closed-loop verification pipeline comprising re-scanning via lint tools and semantic diff matching. Furthermore, we design a novel progressive reinforcement learning strategy enabling cold-start initialization and online iterative refinement, with a dual-axis reward function enforcing both syntactic validity and semantic correctness. Deployed in ByteDance’s production environment, the system serves over 5,000 engineers, has resolved 12,000+ lint issues, achieves weekly active users exceeding 1,000, and attains an 85% repair accuracy rate.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Automate lint error resolution in large-scale enterprise codebases
• Enhance development efficiency by reducing manual remediation efforts
• Address technical debt accumulation from unresolved lint errors

Innovation

• LLM-driven automated lint error remediation workflow
• Progressive reinforcement learning for model training
• Rule-based reward mechanism for patch quality