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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/s10664-015-9381-9openalex-snapshot
An empirical study of the impact of modern code review practices on software quality
inferredcites →https://doi.org/10.1007/s10664-016-9452-6openalex-snapshot
Review participation in modern code review
inferredcites →https://doi.org/10.1007/s10664-018-9602-0openalex-snapshot
Early prediction of merged code changes to prioritize reviewing tasks
inferredcites →https://doi.org/10.1007/s10664-018-9676-8openalex-snapshot
Associating working memory capacity and code change ordering with code review performance
inferredcites →https://doi.org/10.1109/ase.2017.8115626openalex-snapshot
Automatically generating commit messages from diffs using neural machine translation
inferredcites →https://doi.org/10.1109/ase.2019.00101openalex-snapshot
CoRA: Decomposing and Describing Tangled Code Changes for Reviewer
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.2013.6606642openalex-snapshot
Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation
inferredcites →https://doi.org/10.1109/icse.2015.76openalex-snapshot
Tricorder: Building a Program Analysis Ecosystem
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/icse43902.2021.00107openalex-snapshot
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
inferredcites →https://doi.org/10.1109/icse43902.2021.00116openalex-snapshot
Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow
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/icsme.2016.65openalex-snapshot
Search-Based Peer Reviewers Recommendation in Modern Code Review
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/msr.2015.21openalex-snapshot
Characteristics of Useful Code Reviews: An Empirical Study at Microsoft
inferredcites →https://doi.org/10.1109/msr.2015.23openalex-snapshot
Investigating Code Review Practices in Defective Files: An Empirical Study of the Qt System
inferredcites →https://doi.org/10.1109/msr.2015.24openalex-snapshot
Partitioning Composite Code Changes to Facilitate Code Review
inferredcites →https://doi.org/10.1109/msr52588.2021.00049openalex-snapshot
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
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/saner53432.2022.00121openalex-snapshot
Where Should I Look at? Recommending Lines that Reviewers Should Pay Attention To
inferredcites →https://doi.org/10.1109/tse.2012.70openalex-snapshot
A large-scale empirical study of just-in-time quality assurance
inferredcites →https://doi.org/10.1109/tse.2020.3023177openalex-snapshot
Predicting Defective Lines Using a Model-Agnostic Technique
inferredcites →https://doi.org/10.1109/tse.2022.3144348openalex-snapshot
DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction
inferredcites →https://doi.org/10.1145/2491411.2491444openalex-snapshot
Convergent contemporary software peer review practices
inferredcites →https://doi.org/10.1145/2593702.2593705openalex-snapshot
Improving code review effectiveness through reviewer recommendations
inferredcites →https://doi.org/10.1145/2597073.2597082openalex-snapshot
Modern code reviews in open-source projects: which problems do they fix?
inferredcites →https://doi.org/10.1145/2950290.2983931openalex-snapshot
T2API: synthesizing API code usage templates from English texts with statistical translation
inferredcites →https://doi.org/10.1145/3106237.3106290openalex-snapshot
Are deep neural networks the best choice for modeling source code?
inferredcites →https://doi.org/10.1145/3238147.3238190openalex-snapshot
Neural-machine-translation-based commit message generation: how far are we?
inferredcites →https://doi.org/10.1145/3338906.3340457openalex-snapshot
Predicting pull request completion time: a case study on large scale cloud services
inferredcites →https://doi.org/10.1145/3368089.3417058openalex-snapshot
IntelliCode compose: code generation using transformer
inferredcites →https://doi.org/10.1145/3379597.3387449openalex-snapshot
Improved Automatic Summarization of Subroutines via Attention to File Context
inferredcites →https://doi.org/10.18653/v1/p16-1162openalex-snapshot
Neural Machine Translation of Rare Words with Subword Units
inferredcites →https://doi.org/10.5281/zenodo.7482719openalex-snapshot
On Learning Meaningful Code Changes via Neural Machine Translation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-95-4445-5_34openalex-snapshot
Evaluate LLMs on Code Review Understanding: A Multi-step Reasoning Benchmark
inferred← citeshttps://doi.org/10.1007/s10515-025-00491-yopenalex-snapshot
CodeDoctor: multi-category code review comment generation
inferred← citeshttps://doi.org/10.1007/s10664-023-10411-xopenalex-snapshot
What makes a code review useful to OpenDev developers? An empirical investigation
inferred← citeshttps://doi.org/10.1007/s10664-024-10575-0openalex-snapshot
Prioritizing code review requests to improve review efficiency: a simulation study
inferred← citeshttps://doi.org/10.1007/s10664-025-10781-4openalex-snapshot
The price of precision: the cost of preprocessing for automated code revision in code review
inferred← citeshttps://doi.org/10.1007/s10664-026-10848-wopenalex-snapshot
Developers and generative AI: A study of self-admitted usage in open source projects
inferred← citeshttps://doi.org/10.1007/s13278-026-01596-2openalex-snapshot
Do not review my code harshly: identifying counterproductive behavior in code review comments
inferred← citeshttps://doi.org/10.1016/j.cola.2026.101396openalex-snapshot
Automated code review systems using CodeBERT with multi-anchor space-aware temporal convolutional neural network
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.2024.107547openalex-snapshot
Don’t forget to change these functions! recommending co-changed functions in modern code review
inferred← citeshttps://doi.org/10.1016/j.neunet.2026.108650openalex-snapshot
Resolving ambiguity in code refinement via conidfine: A conversationally-Aware framework with disambiguation and targeted retrieval
inferred← citeshttps://doi.org/10.1016/j.softx.2024.101677openalex-snapshot
AICodeReview: Advancing code quality with AI-enhanced reviews
inferred← citeshttps://doi.org/10.1109/aiware69974.2025.00021openalex-snapshot
CFCEval: Evaluating Security Aspects in Code Generated by Large Language Models
inferred← citeshttps://doi.org/10.1109/apsec60848.2023.00026openalex-snapshot
Improving Code Refinement for Code Review Via Input Reconstruction and Ensemble Learning
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00103openalex-snapshot
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00157openalex-snapshot
The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00312openalex-snapshot
What Types of Code Review Comments Do Developers Most Frequently Resolve?
inferred← citeshttps://doi.org/10.1109/caibda65784.2025.11183380openalex-snapshot
Heterogeneous Graph Learning for Code Reviewers Recommendation with LLM's Understanding
inferred← citeshttps://doi.org/10.1109/dsn-s60304.2024.00032openalex-snapshot
Bridging the Gap: A Study of AI-based Vulnerability Management between Industry and Academia
inferred← citeshttps://doi.org/10.1109/icse-seip66354.2025.00036openalex-snapshot
Human-In-The-Loop Software Development Agents
inferred← citeshttps://doi.org/10.1109/icse-seip66354.2025.00043openalex-snapshot
Automated Code Review in Practice
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00203openalex-snapshot
Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00191openalex-snapshot
Intention is All you Need: Refining your Code from your Intention
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00073openalex-snapshot
Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
inferred← citeshttps://doi.org/10.1109/icst69053.2026.00046openalex-snapshot
When Less Is More: Monolingual Fine-Tuning of Language Models for Industrial C# Code Review
inferred← citeshttps://doi.org/10.1109/iscipt67144.2025.11265531openalex-snapshot
Integrating RAG and LLM for Automated Code Review in Practice
inferred← citeshttps://doi.org/10.1109/issre59848.2023.00026openalex-snapshot
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
inferred← citeshttps://doi.org/10.1109/msr66628.2025.00043openalex-snapshot
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
inferred← citeshttps://doi.org/10.1109/saner56733.2023.00036openalex-snapshot
D-ACT: Towards Diff-Aware Code Transformation for Code Review Under a Time-Wise Evaluation
inferred← citeshttps://doi.org/10.1109/saner56733.2023.00075openalex-snapshot
Towards Automated Code Reviews: Does Learning Code Structure Help?
inferred← citeshttps://doi.org/10.1109/saner64311.2025.00070openalex-snapshot
Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study
inferred← citeshttps://doi.org/10.1109/scam59687.2023.00020openalex-snapshot
Explaining Transformer-based Code Models: What Do They Learn? When They Do Not Work?
inferred← citeshttps://doi.org/10.1109/tse.2023.3348172openalex-snapshot
Code Review Automation: Strengths and Weaknesses of the State of the Art
inferred← citeshttps://doi.org/10.1109/tse.2024.3497798openalex-snapshot
GenProgJS: A Baseline System for Test-Based Automated Repair of JavaScript Programs
inferred← citeshttps://doi.org/10.1109/tse.2026.3688237openalex-snapshot
Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions
inferred← citeshttps://doi.org/10.1145/3540250.3549098openalex-snapshot
VulRepair: a T5-based automated software vulnerability repair
inferred← citeshttps://doi.org/10.1145/3540250.3549119openalex-snapshot
CommentFinder: a simpler, faster, more accurate code review comments recommendation
inferred← citeshttps://doi.org/10.1145/3597503.3623306openalex-snapshot
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
inferred← citeshttps://doi.org/10.1145/3597503.3639142openalex-snapshot
Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks
inferred← citeshttps://doi.org/10.1145/3632746openalex-snapshot
Vision Transformer Inspired Automated Vulnerability Repair
inferred← citeshttps://doi.org/10.1145/3641540openalex-snapshot
On the Reliability and Explainability of Language Models for Program Generation
inferred← citeshttps://doi.org/10.1145/3643991.3644910openalex-snapshot
Improving Automated Code Reviews: Learning From Experience
inferred← citeshttps://doi.org/10.1145/3643991.3644918openalex-snapshot
Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
inferred← citeshttps://doi.org/10.1145/3661167.3661183openalex-snapshot
On the Use of ChatGPT for Code Review: Do Developers Like Reviews By ChatGPT?
inferred← citeshttps://doi.org/10.1145/3664602openalex-snapshot
Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection
inferred← citeshttps://doi.org/10.1145/3671016.3674820openalex-snapshot
ERD-CQC : Enhanced Rule and Dependency Code Quality Check for Java
inferred← citeshttps://doi.org/10.1145/3678167openalex-snapshot
Automatically Recommend Code Updates: Are We There Yet?
inferred← citeshttps://doi.org/10.1145/3697013openalex-snapshot
Divide-and-Conquer: Automating Code Revisions via Localization-and-Revision
inferred← citeshttps://doi.org/10.1145/3708518openalex-snapshot
Explaining Explanations: An Empirical Study of Explanations in Code Reviews
inferred← citeshttps://doi.org/10.1145/3708519openalex-snapshot
Automatic Programming: Large Language Models and Beyond
inferred← citeshttps://doi.org/10.1145/3715730openalex-snapshot
Towards Diverse Program Transformations for Program Simplification
inferred← citeshttps://doi.org/10.1145/3729344openalex-snapshot
Unlocking Optimal ORM Database Designs: Accelerated Tradeoff Analysis with Transformers
inferred← citeshttps://doi.org/10.1145/3748647openalex-snapshot
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
inferred← citeshttps://doi.org/10.1145/3762183openalex-snapshot
Leveraging Reviewer Experience in Code Review Comment Generation
inferred← citeshttps://doi.org/10.1145/3779056openalex-snapshot
Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
inferred← citeshttps://doi.org/10.1145/3800963openalex-snapshot
A Roadmap for Modern Code Review: Challenges and Opportunities
inferred← citeshttps://doi.org/10.21655/ijsi.1673-7288.00364openalex-snapshot
Exploration and Improvement of Capabilities of LLMs in Code Refinement Task
inferred← citeshttps://doi.org/10.3389/fcomp.2023.1178040openalex-snapshot
How social interactions can affect Modern Code Review
inferred← citeshttps://doi.org/10.3390/electronics13040767openalex-snapshot
AI-Assisted Programming Tasks Using Code Embeddings and Transformers
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