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Divide-and-Conquer: Automating Code Revisions via Localization-and-Revision
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11219-006-9219-1openalex-snapshot
Concise and consistent naming
inferredcites →https://doi.org/10.1016/j.infsof.2016.01.004openalex-snapshot
Reviewer recommendation for pull-requests in GitHub: What can we learn from code review and bug assignment?
inferredcites →https://doi.org/10.1109/ase.2015.74openalex-snapshot
Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code (T)
inferredcites →https://doi.org/10.1109/ase51524.2021.9678724openalex-snapshot
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
inferredcites →https://doi.org/10.1109/esem.2013.23openalex-snapshot
Impact of Peer Code Review on Peer Impression Formation: A Survey
inferredcites →https://doi.org/10.1109/icpc.2006.16openalex-snapshot
Classifying Change Types for Qualifying Change Couplings
inferredcites →https://doi.org/10.1109/icpc58990.2023.00026openalex-snapshot
Interpretation-based Code Summarization
inferredcites →https://doi.org/10.1109/icpc58990.2023.00036openalex-snapshot
Generation-based Code Review Automation: How Far Are We?
inferredcites →https://doi.org/10.1109/icse.2013.6606617openalex-snapshot
Expectations, outcomes, and challenges of modern code review
inferredcites →https://doi.org/10.1109/icse.2015.131openalex-snapshot
Code Reviews Do Not Find Bugs. How the Current Code Review Best Practice Slows Us Down
inferredcites →https://doi.org/10.1109/icse.2019.00021openalex-snapshot
On Learning Meaningful Code Changes Via Neural Machine Translation
inferredcites →https://doi.org/10.1109/icse43902.2021.00027openalex-snapshot
Towards Automating Code Review Activities
inferredcites →https://doi.org/10.1109/icse48619.2023.00128openalex-snapshot
Automated Repair of Programs from Large Language Models
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/icse48619.2023.00179openalex-snapshot
SkCoder: A Sketch-based Approach for Automatic Code Generation
inferredcites →https://doi.org/10.1109/icse48619.2023.00180openalex-snapshot
An Empirical Comparison of Pre-Trained Models of Source Code
inferredcites →https://doi.org/10.1109/icsm.2015.7332454openalex-snapshot
Four eyes are better than two: On the impact of code reviews on software quality
inferredcites →https://doi.org/10.1109/issre59848.2023.00026openalex-snapshot
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
inferredcites →https://doi.org/10.1109/ms.2017.265100500openalex-snapshot
Code Reviewing in the Trenches: Challenges and Best Practices
inferredcites →https://doi.org/10.1109/saner.2015.7081824openalex-snapshot
Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review
inferredcites →https://doi.org/10.1109/saner.2015.7081827openalex-snapshot
Do code review practices impact design quality? A case study of the Qt, VTK, and ITK projects
inferredcites →https://doi.org/10.1109/saner.2018.8330203openalex-snapshot
Dissection of a bug dataset: Anatomy of 395 patches from Defects4J
inferredcites →https://doi.org/10.1109/saner48275.2020.9054794openalex-snapshot
CORE: Automating Review Recommendation for Code Changes
inferredcites →https://doi.org/10.1109/tse.2017.2755013openalex-snapshot
Automatic Software Repair: A Survey
inferredcites →https://doi.org/10.1109/tse.2019.2892102openalex-snapshot
An Empirical Study of Fault Localization Families and Their Combinations
inferredcites →https://doi.org/10.1109/tse.2022.3185458openalex-snapshot
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach
inferredcites →https://doi.org/10.1145/2491411.2491444openalex-snapshot
Convergent contemporary software peer review practices
inferredcites →https://doi.org/10.1145/2491411.2494584openalex-snapshot
Lexical statistical machine translation for language migration
inferredcites →https://doi.org/10.1145/2594458openalex-snapshot
Peer Review on Open-Source Software Projects
inferredcites →https://doi.org/10.1145/2597073.2597076openalex-snapshot
The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects
inferredcites →https://doi.org/10.1145/2610384.2628055openalex-snapshot
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
inferredcites →https://doi.org/10.1145/2970276.2970283openalex-snapshot
CORRECT: code reviewer recommendation at GitHub for Vendasta technologies
inferredcites →https://doi.org/10.1145/3324884.3416590openalex-snapshot
Automated patch correctness assessment
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/10.1145/3377811.3380338openalex-snapshot
On the efficiency of test suite based program repair
inferredcites →https://doi.org/10.1145/3505247openalex-snapshot
Context-Aware Code Change Embedding for Better Patch Correctness Assessment
inferredcites →https://doi.org/10.1145/3510003.3510621openalex-snapshot
Using pre-trained models to boost code review automation
inferredcites →https://doi.org/10.1145/3533767.3534390openalex-snapshot
An extensive study on pre-trained models for program understanding and generation
inferredcites →https://doi.org/10.1145/3540250.3549081openalex-snapshot
Automating code review activities by large-scale pre-training
inferredcites →https://doi.org/10.1145/3540250.3549119openalex-snapshot
CommentFinder: a simpler, faster, more accurate code review comments recommendation
inferredcites →https://doi.org/10.1145/3540250.3549162openalex-snapshot
NatGen: generative pre-training by “naturalizing” source code
inferredcites →https://doi.org/10.1145/3551349.3556955openalex-snapshot
CoditT5: Pretraining for Source Code and Natural Language Editing
inferredcites →https://doi.org/10.1145/3597203openalex-snapshot
Pre-implementation Method Name Prediction for Object-oriented Programming
inferredcites →https://doi.org/10.1145/3597503.3608134openalex-snapshot
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context 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/10.1145/3611643.3613892openalex-snapshot
InferFix: End-to-End Program Repair with LLMs
inferredcites →https://doi.org/10.1145/3611643.3616323openalex-snapshot
Natural Language to Code: How Far Are We?
inferredcites →https://doi.org/10.1145/3611643.3616339openalex-snapshot
CCT5: A Code-Change-Oriented Pre-trained Model
inferredcites →https://doi.org/10.1145/3622815openalex-snapshot
Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network
inferredcites →https://doi.org/10.1145/3650212.3680381openalex-snapshot
One Size Does Not Fit All: Multi-granularity Patch Generation for Better Automated Program Repair
inferredcites →https://doi.org/10.13182/nse70-a19705openalex-snapshot
The Art of Computer Programming
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.2307/3001968openalex-snapshot
Individual Comparisons by Ranking Methods
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-04200-2_9openalex-snapshot
Leveraging Large Language Models for Software Defect Detection
inferred← citeshttps://doi.org/10.1007/s10115-025-02383-9openalex-snapshot
Advancements in automated program repair: a comprehensive review
inferred← citeshttps://doi.org/10.1007/s10515-025-00549-xopenalex-snapshot
Assessing the effectiveness of recent closed-source large language models in fault localization and automated program repair
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130991openalex-snapshot
CSLLM: Code-Specific Large Language Models—A Survey
inferred← citeshttps://doi.org/10.1109/ecitech69277.2026.11601893openalex-snapshot
PowerRecall: A Knowledge Graph Enhanced Heterogeneous Multi-Path Retrieval Framework for Power Software Code Review
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00049openalex-snapshot
Model Editing for LLMs4Code: How Far are we?
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00104openalex-snapshot
Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering
inferred← citeshttps://doi.org/10.1109/swc65939.2025.00138openalex-snapshot
Efficient Detection of Concurrency Bugs Based on Equivalent Thread Interleaving Priorities
inferred← citeshttps://doi.org/10.1145/3799693openalex-snapshot
A Systematic Literature Review on Large Language Models for Automated Program Repair
inferred← citeshttps://doi.org/10.1145/3808197openalex-snapshot
Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
inferred← citeshttps://doi.org/10.1145/3820059openalex-snapshot
Code Refinement with Repository Context: How Far are We?
inferred← citeshttps://doi.org/10.1145/3832193openalex-snapshot
Just Old Wine in a New Bottle: Improving Vulnerability Repair Robustness to Combat Homologous but Heterogeneous Vulnerabilities
inferred← citeshttps://doi.org/10.1145/3832783.3837493openalex-snapshot
CiL-Agent: Compiler-in-the-Loop LLM Agent for Memory-Safe C Program Repair
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