Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.18398 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
by: Wu, Hao, et al.
Published: (2025)
by: Wu, Hao, et al.
Published: (2025)
Cross-Inlining Binary Function Similarity Detection
by: Jia, Ang, et al.
Published: (2024)
by: Jia, Ang, et al.
Published: (2024)
CRPWarner: Warning the Risk of Contract-related Rug Pull in DeFi Smart Contracts
by: Lin, Zewei, et al.
Published: (2024)
by: Lin, Zewei, et al.
Published: (2024)
SoK: Comprehensive Analysis of Rug Pull Causes, Datasets, and Detection Tools in DeFi
by: Sun, Dianxiang, et al.
Published: (2024)
by: Sun, Dianxiang, et al.
Published: (2024)
ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
by: Peng, Chenyang, et al.
Published: (2025)
by: Peng, Chenyang, et al.
Published: (2025)
Empirical Analysis of Pull Requests for Google Summer of Code
by: Popoola, Saheed
Published: (2024)
by: Popoola, Saheed
Published: (2024)
On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
by: Watanabe, Miku, et al.
Published: (2025)
by: Watanabe, Miku, et al.
Published: (2025)
Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
by: Pinna, Giovanni, et al.
Published: (2026)
by: Pinna, Giovanni, et al.
Published: (2026)
Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests
by: Yoshioka, Haruhiko, et al.
Published: (2026)
by: Yoshioka, Haruhiko, et al.
Published: (2026)
Detecting Essence Code Clones via Information Theoretic Analysis
by: Zhao, Lida, et al.
Published: (2025)
by: Zhao, Lida, et al.
Published: (2025)
Learning to Commit: Generating Organic Pull Requests via Online Repository Memory
by: Li, Mo, et al.
Published: (2026)
by: Li, Mo, et al.
Published: (2026)
How Do Developers Use Code Suggestions in Pull Request Reviews?
by: Bouraffa, Abir, et al.
Published: (2025)
by: Bouraffa, Abir, et al.
Published: (2025)
CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
by: Dou, Shihan, et al.
Published: (2024)
by: Dou, Shihan, et al.
Published: (2024)
On the Effect of Token Merging on Pre-trained Models for Code
by: Saad, Mootez, et al.
Published: (2025)
by: Saad, Mootez, et al.
Published: (2025)
Quality Gatekeepers: Investigating the Effects ofCode Review Bots on Pull Request Activities
by: Wessel, Mairieli, et al.
Published: (2021)
by: Wessel, Mairieli, et al.
Published: (2021)
Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
by: Gong, Jingzhi, et al.
Published: (2026)
by: Gong, Jingzhi, et al.
Published: (2026)
Lightweight Vulnerability Detection from Code Metrics and Token Features
by: Chiu, Chun Yin
Published: (2026)
by: Chiu, Chun Yin
Published: (2026)
Unveiling Project-Specific Bias in Neural Code Models
by: Li, Zhiming, et al.
Published: (2022)
by: Li, Zhiming, et al.
Published: (2022)
SourceP: Detecting Ponzi Schemes on Ethereum with Source Code
by: Lu, Pengcheng, et al.
Published: (2023)
by: Lu, Pengcheng, et al.
Published: (2023)
From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
by: Chowdhury, Kowshik, et al.
Published: (2026)
by: Chowdhury, Kowshik, et al.
Published: (2026)
Do Autonomous Agents Contribute Test Code? A Study of Tests in Agentic Pull Requests
by: Haque, Sabrina, et al.
Published: (2026)
by: Haque, Sabrina, et al.
Published: (2026)
Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
by: Jo, Young, et al.
Published: (2026)
by: Jo, Young, et al.
Published: (2026)
Boosting End-to-End Database Isolation Checking via Mini-Transactions (Extended Version)
by: Wei, Hengfeng, et al.
Published: (2025)
by: Wei, Hengfeng, et al.
Published: (2025)
GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion
by: Wang, Xingliang, et al.
Published: (2025)
by: Wang, Xingliang, et al.
Published: (2025)
Unveiling Memorization in Code Models
by: Yang, Zhou, et al.
Published: (2023)
by: Yang, Zhou, et al.
Published: (2023)
CONNECTOR: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-chain Transaction Association
by: Lin, Dan, et al.
Published: (2024)
by: Lin, Dan, et al.
Published: (2024)
Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests
by: Pham, Dung, et al.
Published: (2026)
by: Pham, Dung, et al.
Published: (2026)
Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
by: Peralta, Sien Reeve O., et al.
Published: (2026)
by: Peralta, Sien Reeve O., et al.
Published: (2026)
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
by: Watanabe, Kan, et al.
Published: (2026)
by: Watanabe, Kan, et al.
Published: (2026)
Unveiling Code Clones in the Eclipse IIoT Software Ecosystem
by: Li, Zengyang, et al.
Published: (2026)
by: Li, Zengyang, et al.
Published: (2026)
From Transactions to Exploits: Automated PoC Synthesis for Real-World DeFi Attacks
by: Su, Xing, et al.
Published: (2026)
by: Su, Xing, et al.
Published: (2026)
Efficient Code Analysis via Graph-Guided Large Language Models
by: Gao, Hang, et al.
Published: (2026)
by: Gao, Hang, et al.
Published: (2026)
The Value of Effective Pull Request Description
by: Pirouzkhah, Shirin, et al.
Published: (2026)
by: Pirouzkhah, Shirin, et al.
Published: (2026)
Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests
by: Cynthia, Shamse Tasnim, et al.
Published: (2026)
by: Cynthia, Shamse Tasnim, et al.
Published: (2026)
Unlocking the Power of Numbers: Log Compression via Numeric Token Parsing
by: Yu, Siyu, et al.
Published: (2024)
by: Yu, Siyu, et al.
Published: (2024)
Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
by: Almukhtar, Mohamed, et al.
Published: (2026)
by: Almukhtar, Mohamed, et al.
Published: (2026)
Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization
by: Jungwirth, Maximilian, et al.
Published: (2025)
by: Jungwirth, Maximilian, et al.
Published: (2025)
SparseCoder: Advancing Source Code Analysis with Sparse Attention and Learned Token Pruning
by: Yang, Xueqi, et al.
Published: (2023)
by: Yang, Xueqi, et al.
Published: (2023)
SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback
by: Kumar, Deepak
Published: (2026)
by: Kumar, Deepak
Published: (2026)
Token Sugar: Making Source Code Sweeter for LLMs through Token-Efficient Shorthand
by: Sun, Zhensu, et al.
Published: (2025)
by: Sun, Zhensu, et al.
Published: (2025)