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

TL;DR

BitsAI-Fix leverages large language models and reinforcement learning to automate lint error fixing in large enterprise codebases, significantly reducing manual effort and technical debt.

Contribution

The paper introduces a novel LLM-based automated lint error remediation workflow with reinforcement learning and rule-based rewards, tailored for industrial-scale environments.

Findings

Resolved over 12,000 static analysis issues at ByteDance.

Achieved approximately 85% remediation accuracy.

Supported over 5,000 engineers with weekly active adopters.

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.