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

https://doi.org/10.1145/3660773
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/icse43902.2021.00107openalex-snapshot
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00111openalex-snapshot
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00125openalex-snapshot
Impact of Code Language Models on Automated Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00126openalex-snapshot
Tare: Type-Aware Neural Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00127openalex-snapshot
Template-based Neural Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00129openalex-snapshot
Automated Program Repair in the Era of Large Pre-trained Language Models
inferredcites →https://doi.org/10.1109/icse48619.2023.00188openalex-snapshot
An Empirical Study of Deep Learning Models for Vulnerability Detection
inferredcites →https://doi.org/10.1109/taic.part.2007.13openalex-snapshot
On the Accuracy of Spectrum-based Fault Localization
inferredcites →https://doi.org/10.1109/tse.2019.2940179openalex-snapshot
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
inferredcites →https://doi.org/10.1109/tse.2020.3020502openalex-snapshot
CODIT: Code Editing With Tree-Based Neural Models
inferredcites →https://doi.org/10.1145/3105906openalex-snapshot
Automatic Software Repair
inferredcites →https://doi.org/10.1145/3318162openalex-snapshot
Automated program repair
inferredcites →https://doi.org/10.1145/3340544openalex-snapshot
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
inferredcites →https://doi.org/10.1145/3387940.3392181openalex-snapshot
Learning to Fix Build Errors with Graph2Diff Neural Networks
inferredcites →https://doi.org/10.1145/3395363.3397369openalex-snapshot
CoCoNuT: combining context-aware neural translation models using ensemble for program repair
inferredcites →https://doi.org/10.1145/3468264.3468544openalex-snapshot
A syntax-guided edit decoder for neural program repair
inferredcites →https://doi.org/10.1145/3510003.3510222openalex-snapshot
Neural program repair with execution-based backpropagation
inferredcites →https://doi.org/10.1145/3540250.3549101openalex-snapshot
Less training, more repairing please: revisiting automated program repair via zero-shot learning
inferredcites →https://doi.org/10.1145/3597926.3598067openalex-snapshot
Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
inferredcites →https://doi.org/10.1145/3597926.3598135openalex-snapshot
How Effective Are Neural Networks for Fixing Security Vulnerabilities
inferredcites →https://doi.org/10.1145/3611643.3616265openalex-snapshot
Neural-Based Test Oracle Generation: A Large-Scale Evaluation and Lessons Learned
inferredcites →https://doi.org/10.18653/v1/2021.nlp4prog-1.5openalex-snapshot
CoTexT: Multi-task Learning with Code-Text Transformer
inferredcites →https://doi.org/10.48550/arxiv.2207.10397openalex-snapshot
CodeT: Code Generation with Generated Tests

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-86149-9_42openalex-snapshot
Leveraging Prompt-Based Large Language Models for Code Smell Detection: A Comparative Study on the MLCQ Dataset
inferred← citeshttps://doi.org/10.1007/s10515-025-00553-1openalex-snapshot
BRMDS: an LLM-based multi-dimensional summary generation approach for bug reports
inferred← citeshttps://doi.org/10.1007/s10515-025-00556-yopenalex-snapshot
Graph neural networks for precise bug localization through structural program analysis
inferred← citeshttps://doi.org/10.1007/s10515-026-00649-2openalex-snapshot
CoFiLoc: A Coarse-to-fine LLM-based framework for method-level bug localization
inferred← citeshttps://doi.org/10.1007/s10664-025-10717-yopenalex-snapshot
Towards understanding the impact of data bugs on deep learning models in software engineering
inferred← citeshttps://doi.org/10.1007/s10664-025-10745-8openalex-snapshot
What characteristics make ChatGPT effective for software issue resolution? An empirical study of task, project, and conversational signals in GitHub issues
inferred← citeshttps://doi.org/10.1007/s10664-026-10833-3openalex-snapshot
HAFix: history-augmented large language models for bug fixing
inferred← citeshttps://doi.org/10.1007/s40998-026-01210-3openalex-snapshot
Using the Textual Features of Bug Reports to Localize the Related Source Code with an Emphasis on Semantics
inferred← citeshttps://doi.org/10.1016/j.cosrev.2025.100732openalex-snapshot
Characterising harmful API uses and repair techniques: Insights from a systematic review
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130991openalex-snapshot
CSLLM: Code-Specific Large Language Models—A Survey
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107523openalex-snapshot
Fine-tuning and prompt engineering for large language models-based code review automation
inferred← citeshttps://doi.org/10.1016/j.infsof.2025.107758openalex-snapshot
Benchmarking large language models for automated labeling: The case of issue report classification
inferred← citeshttps://doi.org/10.1016/j.infsof.2025.108010openalex-snapshot
FlowRepair: Search-based automated program repair of CPS controllers modeled in Simulink-Stateflow
inferred← citeshttps://doi.org/10.1016/j.infsof.2026.108282openalex-snapshot
CAMP: Cognition-Aligned Multi-stage Automated Program Repair
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112581openalex-snapshot
Towards higher quality software vulnerability data using LLM-based patch filtering
inferred← citeshttps://doi.org/10.1016/j.jss.2026.112851openalex-snapshot
Issue classification with LLMs: An empirical study of the NASA flight software systems
inferred← citeshttps://doi.org/10.1016/j.jss.2026.113015openalex-snapshot
Unveiling the power of code pre-trained models in automated program repair: A systematic review
inferred← citeshttps://doi.org/10.1109/access.2026.3665337openalex-snapshot
An MCP Tool for Bug Localization by Leveraging Modification Frequencies of Similar Bug Reports
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00117openalex-snapshot
Learning Project-wise Subsequent Code Edits via Interleaving Neural-based Induction and Tool-based Deduction
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00137openalex-snapshot
Characterizing Multi-Hunk Patches: Divergence, Proximity, and LLM Repair Challenges
inferred← citeshttps://doi.org/10.1109/dac63849.2025.11133280openalex-snapshot
Location is Key: Leveraging LLM for Functional Bug Localization in Verilog Design
inferred← citeshttps://doi.org/10.1109/dsa66321.2025.00021openalex-snapshot
Review of Large Language Model-Based Software Fault Localization Techniques
inferred← citeshttps://doi.org/10.1109/dsa66321.2025.00024openalex-snapshot
Large Language Models for Software Fault Localization: a Survey
inferred← citeshttps://doi.org/10.1109/dsn69566.2026.00026openalex-snapshot
LUMEN: A Systems Approach to LLM-Guided Activation of Hidden Behaviors in Malware
inferred← citeshttps://doi.org/10.1109/iccraids67816.2026.11519702openalex-snapshot
Adaptive Shell Scripting with Quine Programs and Large Language Models
inferred← citeshttps://doi.org/10.1109/iceconf65644.2025.11379560openalex-snapshot
Large Language Models-Driven Bug Localization Framework for Automated Debugging
inferred← citeshttps://doi.org/10.1109/icse-companion66252.2025.00038openalex-snapshot
Foundation Models for Automatic Issue Labeling
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00098openalex-snapshot
TOGLL: Correct and Strong Test Oracle Generation with LLMS
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00215openalex-snapshot
Treefix: Enabling Execution with a Tree of Prefixes
inferred← citeshttps://doi.org/10.1109/icsess67729.2025.11380277openalex-snapshot
A Survey and Prospects of Source Code Processing Techniques Based on Large Language Models
inferred← citeshttps://doi.org/10.1109/idciot64235.2025.10914819openalex-snapshot
Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs
inferred← citeshttps://doi.org/10.1109/inmic65900.2025.11348399openalex-snapshot
Multi-Agent Systems in Software Testing: From Planning to Reporting
inferred← citeshttps://doi.org/10.1109/llm4code66737.2025.00023openalex-snapshot
Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs
inferred← citeshttps://doi.org/10.1109/msr66628.2025.00086openalex-snapshot
Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
inferred← citeshttps://doi.org/10.1109/msr66628.2025.00107openalex-snapshot
Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution
inferred← citeshttps://doi.org/10.1109/qrs65678.2025.00015openalex-snapshot
Generation, Migration and Optimization of Cross-Language Static-Analysis Rules Based on Large Language Models
inferred← citeshttps://doi.org/10.1109/saner67736.2026.00107openalex-snapshot
Multi-CoLoR: Context-Aware Localization and Reasoning Across Multi-Language Codebases
inferred← citeshttps://doi.org/10.1109/scse65633.2025.11030993openalex-snapshot
Enhancing Flutter App Development: Addressing Configuration and Compatibility Bugs Using Large Language Models
inferred← citeshttps://doi.org/10.1109/snpd65828.2025.11253974openalex-snapshot
Evaluating the Source Code Review Performance of LLM-based AI Chatbots
inferred← citeshttps://doi.org/10.1109/spw67851.2025.00040openalex-snapshot
Code Vulnerability Repair with Large Language Model Using Context-Aware Prompt Tuning
inferred← citeshttps://doi.org/10.1109/tifs.2025.3618407openalex-snapshot
InstructRepair: Instruct Large Language Models With Rich Bug Information for Automated Program Repair
inferred← citeshttps://doi.org/10.1109/tse.2024.3420886openalex-snapshot
Automated Refactoring of Non-Idiomatic Python Code With Pythonic Idioms
inferred← citeshttps://doi.org/10.1109/tse.2025.3532759openalex-snapshot
Comprehensive Fine-Tuning Large Language Models of Code for Automated Program Repair
inferred← citeshttps://doi.org/10.1109/tse.2025.3581062openalex-snapshot
RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
inferred← citeshttps://doi.org/10.1109/tse.2025.3599581openalex-snapshot
Vercation: Precise Vulnerable Open-Source Software Version Identification Based on Static Analysis and LLM
inferred← citeshttps://doi.org/10.1142/s0218194025501153openalex-snapshot
Bug Localization Based on Context-Aware Code Translation and Feature Fusion
inferred← citeshttps://doi.org/10.1145/3696630.3728603openalex-snapshot
CodingGenie: A Proactive LLM-Powered Programming Assistant
inferred← citeshttps://doi.org/10.1145/3707457openalex-snapshot
A Catalog of Data Smells for Coding Tasks
inferred← citeshttps://doi.org/10.1145/3715007openalex-snapshot
An Empirical Study on Challenges for LLM Application Developers
inferred← citeshttps://doi.org/10.1145/3715751openalex-snapshot
HornBro: Homotopy-Like Method for Automated Quantum Program Repair
inferred← citeshttps://doi.org/10.1145/3716489.3728432openalex-snapshot
On the Attitudes of GameDev Industry Artists towards GenAI. Preliminary Results
inferred← citeshttps://doi.org/10.1145/3719664openalex-snapshot
A Review on Edge Large Language Models: Design, Execution, and Applications
inferred← citeshttps://doi.org/10.1145/3729354openalex-snapshot
Doc2OracLL: Investigating the Impact of Documentation on LLM-Based Test Oracle Generation
inferred← citeshttps://doi.org/10.1145/3733965.3733969openalex-snapshot
Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection
inferred← citeshttps://doi.org/10.1145/3734523openalex-snapshot
LHS: LLM Assisted Efficient High-level Synthesis of Deep Learning Tasks
inferred← citeshttps://doi.org/10.1145/3735950.3735954openalex-snapshot
SecureMind: A Framework for Benchmarking Large Language Models in Memory Bug Detection and Repair
inferred← citeshttps://doi.org/10.1145/3742876openalex-snapshot
Proceedings of the 24th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences
inferred← citeshttps://doi.org/10.1145/3742876.3742881openalex-snapshot
Comparative Analysis of Pre-trained Code Language Models for Automated Program Repair via Code Infill Generation
inferred← citeshttps://doi.org/10.1145/3744916.3773219openalex-snapshot
Are Humans and LLMs Confused by the Same Code? An Empirical Study on Fixation-Related Potentials and LLM Perplexity
inferred← citeshttps://doi.org/10.1145/3749840openalex-snapshot
AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model
inferred← citeshttps://doi.org/10.1145/3756681.3756991openalex-snapshot
PRIMG : Efficient LLM-driven Test Generation Using Mutant Prioritization
inferred← citeshttps://doi.org/10.1145/3756681.3756999openalex-snapshot
LAMeD: LLM-generated Annotations for Memory Leak Detection
inferred← citeshttps://doi.org/10.1145/3764584openalex-snapshot
Patch Generation in APR: A Survey from the Perspectives of Utilizing LLMs and Using APR-Specific Information
inferred← citeshttps://doi.org/10.1145/3769676openalex-snapshot
Large Language Models for Cyber Security: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1145/3770084openalex-snapshot
A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
inferred← citeshttps://doi.org/10.1145/3786175.3788347openalex-snapshot
Multicalibration for LLM-based Code Generation
inferred← citeshttps://doi.org/10.1145/3793302.3793346openalex-snapshot
PhantomRun: Auto Repair of Compilation Errors in Embedded Open Source Software
inferred← citeshttps://doi.org/10.1145/3794763.3794794openalex-snapshot
On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study
inferred← citeshttps://doi.org/10.1145/3811034openalex-snapshot
Arash : Token-Efficient LLM-Assisted Crash Root Cause Analysis in Fuzz Driver Generation
inferred← citeshttps://doi.org/10.1145/3832272openalex-snapshot
Automated Program Repair for UI-Centric Android Bugs: How Far Are We?
inferred← citeshttps://doi.org/10.1145/3837729.3840485openalex-snapshot
CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation
inferred← citeshttps://doi.org/10.1145/3846194openalex-snapshot
Bug Whisperer: Unleashing LLMs to Craft Context-Aware Tests Guided by Path Constraints and Bug Clues
inferred← citeshttps://doi.org/10.2139/ssrn.5624902openalex-snapshot
Generative transformations and patterns in LLM-native approaches for software verification and falsification
inferred← citeshttps://doi.org/10.23919/csms.2025.0004openalex-snapshot
Learning-Based Automated Program Repair: A Systematic Literature Review
inferred← citeshttps://doi.org/10.2478/cait-2026-0006openalex-snapshot
Automated Bug Detection and Program Repair Using Deep Learning: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/w18101132openalex-snapshot
Large Language Models for Coagulant Dosage Prediction: A Systematic Investigation of Generalization, Rationale Patterns and Hallucination
inferred← citeshttps://doi.org/10.4018/979-8-3373-8252-4.ch001openalex-snapshot
Unmasking Threat Vectors in Large Language Models
inferred← citeshttps://doi.org/10.4018/979-8-3373-8857-1.ch003openalex-snapshot
Artificial Intelligence and Education
inferred← citeshttps://doi.org/10.48084/etasr.18559openalex-snapshot
Improving File-Level Bug Localization Using Pre-Trained Code Models: A Comprehensive Review
inferred← citeshttps://doi.org/10.54254/2755-2721/99/20251769openalex-snapshot
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