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

• URL: http://arxiv.org/abs/2508.03487v1
• Date: Tue, 05 Aug 2025 14:17:30 GMT
• Title: BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
• Authors: Yuanpeng Li, Qi Long, Zhiyuan Yao, Jian Xu, Lintao Xie, Xu He, Lu Geng, Xin Han, Yueyan Chen, Wenbo Duan,
• Abstract summary: BitsAI-Fix is an automated lint error remediation workflow based on Large Language Models (LLMs)<n>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.
• Score: 11.767390004985979
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• $\ abla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space [71.23672814629448]
  $nabla$-Reasoner is an iterative generation framework that integrates differentiable optimization over token logits into the decoding loop.<n>$nabla$-Reasoner achieves over 20% accuracy improvement on a challenging mathematical reasoning benchmark.
  arXiv  Detail & Related papers  (2026-03-05T08:42:54Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents [1.991571265620589]
  Recent advances in code generation models have unlocked unprecedented opportunities for automating feature engineering.<n>Their adoption in real-world ML teams remains constrained by critical challenges.<n>We address these challenges with a planner-guided, constrained-topology multi-agent framework.
  arXiv  Detail & Related papers  (2026-01-15T19:33:42Z)
• AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolution [20.91584024250844]
  AutoICE is an evolutionary search for synthesizing verifiable C code.<n>It successfully verifies $90.36$% of code, outperforming the state-of-the-art (SOTA) approach.
  arXiv  Detail & Related papers  (2025-12-08T12:35:10Z)
• DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation [68.19756761027351]
  Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models.<n>We investigate their denoising processes and reinforcement learning methods.<n>Our work provides deeper insight into the machinery of dLLM generation and offers an effective, diffusion-native RL training framework.
  arXiv  Detail & Related papers  (2025-06-25T17:35:47Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• LADs: Leveraging LLMs for AI-Driven DevOps [3.240228178267042]
  LADs is a principled approach to configuration optimization through in-depth analysis of what optimization works under which conditions.<n>By leveraging Retrieval-Augmented Generation, Few-Shot Learning, Chain-of-Thought, and Feedback-Based Prompt Chaining, LADs generates accurate configurations and learns from deployment failures to iteratively refine system settings.<n>Our findings reveal key insights into the trade-offs between performance, cost, and scalability, helping practitioners determine the right strategies for different deployment scenarios.
  arXiv  Detail & Related papers  (2025-02-28T08:12:08Z)
• LABIIUM: AI-Enhanced Zero-configuration Measurement Automation System [0.0]
  We present LABIIUM, an AI-enhanced measurement automation system designed to streamline experimental and improve user productivity.<n>Lab-Automation-Measurement Bridges (LAMBs) enable seamless instrument connectivity using standard tools such as VSCode and Python, eliminating setup overhead.<n>The evaluation underscores LABIIUM's ability to enhance laboratory productivity and support digital transformation in research and industry.
  arXiv  Detail & Related papers  (2024-12-07T00:15:24Z)
• Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach [0.7420433640907689]
  We encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process.<n>The proposed approach is flexible and applicable to both regression and classification tasks.<n>The design allows for the automation of the loss function by simply updating the ASP rules.
  arXiv  Detail & Related papers  (2024-11-13T09:33:33Z)
• Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling [0.0]
  One promising approach is to train an RL agent as an improvement, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. We benchmarked our approach against other approaches using real data from our industry partner, demonstrating its superior performance.
  arXiv  Detail & Related papers  (2024-09-18T12:48:56Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models [3.1690235522182104]
  Large language models (LLMs) are increasingly used to solve various programming tasks. We show that the task is difficult as it requires the model to learn long-range code relationships. We propose a technique to address these challenges with a new approach for querying and fine-tuning LLMs.
  arXiv  Detail & Related papers  (2024-02-19T18:35:40Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)
• A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification [8.733354577147093]
  This paper introduces an innovative approach that combines Large Language Models (LLMs) with Formal Verification strategies for automatic software vulnerability repair. We present the ESBMC-AI framework as a proof of concept, leveraging the well-recognized and industry-adopted Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained transformer model. Our results demonstrate ESBMC-AI's capability to automate the detection and repair of issues such as buffer overflow, arithmetic overflow, and pointer dereference failures with high accuracy.
  arXiv  Detail & Related papers  (2023-05-24T05:54:10Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.