On the public citation graph
Fine-Tuning Large Language Models to Improve Accuracy and Comprehensibility of Automated Code Review
0 signed · 60 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11704-024-40231-1openalex-snapshot
A survey on large language model based autonomous agents
inferredcites →https://doi.org/10.1016/j.jss.2023.111741openalex-snapshot
Out of the BLEU: How should we assess quality of the Code Generation models?
inferredcites →https://doi.org/10.1073/pnas.2305016120openalex-snapshot
ChatGPT outperforms crowd workers for text-annotation tasks
inferredcites →https://doi.org/10.1080/14786440009463897openalex-snapshot
X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
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/icse43902.2021.00027openalex-snapshot
Towards Automating Code Review Activities
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/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/tse.2015.2500238openalex-snapshot
Automatically Recommending Peer Reviewers in Modern Code Review
inferredcites →https://doi.org/10.1145/2568225.2568260openalex-snapshot
An exploratory study of the pull-based software development model
inferredcites →https://doi.org/10.1145/3368089.3417058openalex-snapshot
IntelliCode compose: code generation using transformer
inferredcites →https://doi.org/10.1145/3510003openalex-snapshot
Proceedings of the 44th International Conference on Software Engineering
inferredcites →https://doi.org/10.1145/3540250.3549081openalex-snapshot
Automating code review activities by large-scale pre-training
inferredcites →https://doi.org/10.1162/tacl_a_00632openalex-snapshot
Benchmarking Large Language Models for News Summarization
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014910openalex-snapshot
Automatic Code Review by Learning the Revision of Source Code
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/10.18653/v1/2023.acl-long.411openalex-snapshot
Large Language Models Meet NL2Code: A Survey
inferredcites →https://doi.org/10.18653/v1/2023.ijcnlp-main.45openalex-snapshot
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
inferredcites →https://doi.org/10.18653/v1/n18-1119openalex-snapshot
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1904.09751openalex-snapshot
The Curious Case of Neural Text Degeneration
inferredcites →https://doi.org/10.48550/arxiv.2002.08155openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.48550/arxiv.2106.09685openalex-snapshot
LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency
inferredcites →https://doi.org/10.48550/arxiv.2201.11903openalex-snapshot
BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
inferredcites →https://doi.org/10.48550/arxiv.2203.02155openalex-snapshot
Discernment and Social Learning as a Companion Training Layer
inferredcites →https://doi.org/10.55529/ijitc.31.17.22openalex-snapshot
Use Chat GPT to Solve Programming Bugs
inferredcites →https://doi.org/10.7759/cureus.40895openalex-snapshot
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/aet2.70189openalex-snapshot
Using Generative AI to Appraise the Quality of Medical Education Research Studies: Agreement Between AI ‐Generated and Human MERSQI Scores
inferred← citeshttps://doi.org/10.1007/978-3-032-16649-4_2openalex-snapshot
Bridging Semantics and Structure for Software Vulnerability Detection Using Hybrid Network Models
inferred← citeshttps://doi.org/10.1007/s10515-025-00491-yopenalex-snapshot
CodeDoctor: multi-category code review comment generation
inferred← citeshttps://doi.org/10.1007/s10664-025-10791-2openalex-snapshot
Code review as decision-making - building a cognitive model from the questions asked during code review
inferred← citeshttps://doi.org/10.1016/j.ymssp.2025.113226openalex-snapshot
FD-MVLLM: Fault diagnosis based on multimodal vibration data and large language model for bearing system
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00315openalex-snapshot
SGCR: A Specification-Grounded Framework for Trustworthy LLM Code Review
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11253620openalex-snapshot
Leveraging Large Language Models for Automated Feature Extraction and Model Training in EMG-Based Motion Decoding
inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491067openalex-snapshot
Code Poisoning Through Misleading Comments: Jailbreaking Large Language Models via Contextual Deception
inferred← citeshttps://doi.org/10.1109/icse-seip66354.2025.00012openalex-snapshot
Dialogagent: An Auto-Engagement Agent for Code Question Answering Data Production
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00042openalex-snapshot
The Impact of Fine-Tuning Large Language Models on Automated Program Repair
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00048openalex-snapshot
Can Llms Update Api Documentation?
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00061openalex-snapshot
Automated Code Review Using Large Language Models at Ericsson: An Experience Report
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/issre66568.2025.00033openalex-snapshot
Breaking Task Isolation: Enhancing Code Review Automation with Mixture-of-Experts Large Language Models
inferred← citeshttps://doi.org/10.1109/qrs-c65679.2025.00029openalex-snapshot
Software Defects Prediction: Source Code Is All You Need
inferred← citeshttps://doi.org/10.1109/sami68106.2026.11420586openalex-snapshot
Large Language Models in Software Documentation and Modeling: A Literature Review and Findings
inferred← citeshttps://doi.org/10.1109/swc65939.2025.00123openalex-snapshot
HybridLog: LLM-Powered Log Anomaly Analysis with Heterogeneous Semantic Collaboration Training
inferred← citeshttps://doi.org/10.1109/tse.2025.3591934openalex-snapshot
CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection
inferred← citeshttps://doi.org/10.1109/wi-iat67162.2025.00114openalex-snapshot
Memory Meets Style: Adaptive Fine-Tuning and Contrastive RAG for Smart Code Review Automation
inferred← citeshttps://doi.org/10.1145/3696630.3728505openalex-snapshot
Support, Not Automation: Towards AI-supported Code Review For Code Quality and Beyond
inferred← citeshttps://doi.org/10.1145/3696630.3728552openalex-snapshot
BitsAI-CR: Automated Code Review via LLM in Practice
inferred← citeshttps://doi.org/10.1145/3715007openalex-snapshot
An Empirical Study on Challenges for LLM Application Developers
inferred← citeshttps://doi.org/10.1145/3744916.3764557openalex-snapshot
SeRe: A Security-Related Code Review Dataset Aligned with Real-World Review Activities
inferred← citeshttps://doi.org/10.1145/3744916.3773191openalex-snapshot
SecureReviewer: Enhancing Large Language Models for Secure Code Review through Secure-Aware Fine-Tuning
inferred← citeshttps://doi.org/10.1145/3772318.3791428openalex-snapshot
Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making
inferred← citeshttps://doi.org/10.1145/3796522openalex-snapshot
A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models
inferred← citeshttps://doi.org/10.1145/3797092openalex-snapshot
From Specifications to Implementation in the Gen-AI Era: Lessons from a Project-Based Software Engineering Course
inferred← citeshttps://doi.org/10.1145/3832096openalex-snapshot
E4R-Reviewer: Effective and Explainable Automated Code Review via End-to-End Reasoning-Guided Alignment
inferred← citeshttps://doi.org/10.1145/3832162openalex-snapshot
From Greedy Steps to Global Optimization: Learning Sequential Test Suite Generation
inferred← citeshttps://doi.org/10.1145/3832293openalex-snapshot
Empowering Lightweight Language Models for Security Code Review via Context-Aware Distillation
inferred← citeshttps://doi.org/10.21203/rs.3.rs-6444534/v1openalex-snapshot
The Effectiveness of Local Fine-Tuned LLMs: Assessment of the Japanese National Examination for Pharmacists
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.