# A Deep Dive into Large Language Models for Automated Bug Localization and Repair

arXiv: https://arxiv.org/abs/2404.11595 · HTML: https://mlsanity.com/paper/2404.11595/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2024.

## Built on by (most cited first, 30 of 39)

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- [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024)
- [LeDex: Training LLMs to Better Self-Debug and Explain Code](https://mlsanity.com/paper/2405.18649/) (2024)
- [Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection](https://mlsanity.com/paper/2408.12986/) (2024)
- [QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks](https://mlsanity.com/paper/2501.17167/) (2025)
- [Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code](https://mlsanity.com/paper/2410.09997/) (2024)
- [Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog](https://mlsanity.com/paper/2409.15186/) (2024)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement](https://mlsanity.com/paper/2409.14201/) (2024)
- [Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs](https://mlsanity.com/paper/2501.07857/) (2025)
- [TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code](https://mlsanity.com/paper/2602.06875/) (2026)
- [Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities](https://mlsanity.com/paper/2509.25894/) (2025)
- [Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair](https://mlsanity.com/paper/2412.03905/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging](https://mlsanity.com/paper/2505.15356/) (2025)
- ["You still have to study" -- On the Security of LLM generated code](https://mlsanity.com/paper/2408.07106/) (2024)
- [Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement](https://mlsanity.com/paper/2503.22512/) (2025)
- [Loc2Repair: A Framework for Evaluating the Impact of File-Level Issue Localization in Repo-Level LLM Repair](https://mlsanity.com/paper/2606.30963/) (2026)
- [Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning](https://mlsanity.com/paper/2409.18395/) (2024)
- [Treefix: Enabling Execution with a Tree of Prefixes](https://mlsanity.com/paper/2501.12339/) (2025)
- [WhatsCode: Large-Scale GenAI Deployment for Developer Efficiency at WhatsApp](https://mlsanity.com/paper/2512.05314/) (2025)
- [Multicalibration for LLM-based Code Generation](https://mlsanity.com/paper/2512.08810/) (2025)
- [ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization](https://mlsanity.com/paper/2605.03117/) (2026)
- [PRIMG : Efficient LLM-driven Test Generation Using Mutant Prioritization](https://mlsanity.com/paper/2505.05584/) (2025)
- [Towards Functional Correctness of Large Code Models with Selective Generation](https://mlsanity.com/paper/2505.13553/) (2025)
- [Multi-task Code LLMs: Data Mix or Model Merge?](https://mlsanity.com/paper/2601.21115/) (2026)
- [Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency](https://mlsanity.com/paper/2608.14065/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug fixing utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug locations, rely on line-level localization tools, or address bug prediction and fixing in one step, our approach uniquely employs LLMs to predict bug location at the token level and subsequently utilizes them for bug fixing. This methodological separation of bug localization and fixing using different LLMs enables effective integration of diverse contextual information and improved incorporation of inductive biases. We introduce Toggle: Token-Granulated Bug Localization and Repair, a comprehensive program repair framework that integrates a bug localization model, an adjustment unit, and a bug-fixing model. Toggle takes a buggy function as input and generates a complete corrected function. We investigate various styles of prompting to the bug fixing model to identify the most effective prompts that better utilize the inductive bias and significantly outperform others. Toggle achieves the new state-of-the-art (SOTA) performance on the CodeXGLUE code refinement benchmark, and exhibits better and comparable performance on several other widely-used APR datasets, including Defects4J.