On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices
Fuente:
arXiv
Saved in:
| Main Authors: | Kashif, Syed Mohammad, Liang, Peng, Tahir, Amjed |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
by: Kashif, Syed Mohammad, et al.
Published: (2026)
by: Kashif, Syed Mohammad, et al.
Published: (2026)
A Survey of Bugs in AI-Generated Code
by: Gao, Ruofan, et al.
Published: (2025)
by: Gao, Ruofan, et al.
Published: (2025)
An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
by: Yu, Jiaxin, et al.
Published: (2024)
by: Yu, Jiaxin, et al.
Published: (2024)
FasterPy: An LLM-based Code Execution Efficiency Optimization Framework
by: Wu, Yue, et al.
Published: (2025)
by: Wu, Yue, et al.
Published: (2025)
On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
by: Jahromi, Ali Soltanian Fard, et al.
Published: (2026)
by: Jahromi, Ali Soltanian Fard, et al.
Published: (2026)
From Prompting to Verification: How Experience Shapes Vibe Coding Practices
by: Fawzy, Ahmed, et al.
Published: (2026)
by: Fawzy, Ahmed, et al.
Published: (2026)
Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review
by: Fawzy, Ahmed, et al.
Published: (2025)
by: Fawzy, Ahmed, et al.
Published: (2025)
AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
by: Zhu, Yuecai, et al.
Published: (2026)
by: Zhu, Yuecai, et al.
Published: (2026)
Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey
by: Fawzy, Ahmed, et al.
Published: (2024)
by: Fawzy, Ahmed, et al.
Published: (2024)
AI-Assisted Assessment of Coding Practices in Modern Code Review
by: Vijayvergiya, Manushree, et al.
Published: (2024)
by: Vijayvergiya, Manushree, et al.
Published: (2024)
SEMAG: Self-Evolutionary Multi-Agent Code Generation
by: Peng, Yulin, et al.
Published: (2026)
by: Peng, Yulin, et al.
Published: (2026)
Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
by: Ashraf, Humza, et al.
Published: (2025)
by: Ashraf, Humza, et al.
Published: (2025)
WebApp1K: A Practical Code-Generation Benchmark for Web App Development
by: Cui, Yi
Published: (2024)
by: Cui, Yi
Published: (2024)
Revisit Self-Debugging with Self-Generated Tests for Code Generation
by: Chen, Xiancai, et al.
Published: (2025)
by: Chen, Xiancai, et al.
Published: (2025)
Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot
by: Zhang, Beiqi, et al.
Published: (2024)
by: Zhang, Beiqi, et al.
Published: (2024)
Paradigm shift on Coding Productivity Using GenAI
by: Yu, Liang
Published: (2025)
by: Yu, Liang
Published: (2025)
Spec-Driven Development:From Code to Contract in the Age of AI Coding Assistants
by: Piskala, Deepak Babu
Published: (2026)
by: Piskala, Deepak Babu
Published: (2026)
Test-Driven Development for Code Generation
by: Mathews, Noble Saji, et al.
Published: (2024)
by: Mathews, Noble Saji, et al.
Published: (2024)
Automated Proof Generation for Rust Code via Self-Evolution
by: Chen, Tianyu, et al.
Published: (2024)
by: Chen, Tianyu, et al.
Published: (2024)
The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution
by: Baqar, Mohammad, et al.
Published: (2026)
by: Baqar, Mohammad, et al.
Published: (2026)
Future of Code with Generative AI: Transparency and Safety in the Era of AI Generated Software
by: Hanson, David
Published: (2025)
by: Hanson, David
Published: (2025)
An Empirical Study of Agent Developer Practices in AI Agent Frameworks
by: Wang, Yanlin, et al.
Published: (2025)
by: Wang, Yanlin, et al.
Published: (2025)
AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
by: Wang, Bin, et al.
Published: (2025)
by: Wang, Bin, et al.
Published: (2025)
AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
by: Oliveira, Eduardo, et al.
Published: (2026)
by: Oliveira, Eduardo, et al.
Published: (2026)
Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education
by: Pan, Wei Hung, et al.
Published: (2024)
by: Pan, Wei Hung, et al.
Published: (2024)
AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions
by: Baqar, Mohammad, et al.
Published: (2025)
by: Baqar, Mohammad, et al.
Published: (2025)
Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs
by: Xie, Chen, et al.
Published: (2025)
by: Xie, Chen, et al.
Published: (2025)
Self-Healing Software Systems: Lessons from Nature, Powered by AI
by: Baqar, Mohammad, et al.
Published: (2025)
by: Baqar, Mohammad, et al.
Published: (2025)
Large Language Model Guided Self-Debugging Code Generation
by: Adnan, Muntasir, et al.
Published: (2025)
by: Adnan, Muntasir, et al.
Published: (2025)
Perceptual Self-Reflection in Agentic Physics Simulation Code Generation
by: Shende, Prashant, et al.
Published: (2026)
by: Shende, Prashant, et al.
Published: (2026)
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs
by: Liu, Zhihan, et al.
Published: (2024)
by: Liu, Zhihan, et al.
Published: (2024)
Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
by: Petrovic, Nenad, et al.
Published: (2025)
by: Petrovic, Nenad, et al.
Published: (2025)
On the Adoption of AI Coding Agents in Open-source Android and iOS Development
by: Khan, Muhammad Ahmad, et al.
Published: (2026)
by: Khan, Muhammad Ahmad, et al.
Published: (2026)
From Inductive to Deductive: LLMs-Based Qualitative Data Analysis in Requirements Engineering
by: Shah, Syed Tauhid Ullah, et al.
Published: (2025)
by: Shah, Syed Tauhid Ullah, et al.
Published: (2025)
Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice
by: Hu, Ruida, et al.
Published: (2025)
by: Hu, Ruida, et al.
Published: (2025)
EvoCodeBench: A Human-Performance Benchmark for Self-Evolving LLM-Driven Coding Systems
by: Zhang, Wentao, et al.
Published: (2026)
by: Zhang, Wentao, et al.
Published: (2026)
Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source
by: Rahman, Musfiqur, et al.
Published: (2026)
by: Rahman, Musfiqur, et al.
Published: (2026)
A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System
by: Grote, Marcel, et al.
Published: (2023)
by: Grote, Marcel, et al.
Published: (2023)
WhatsCode: Large-Scale GenAI Deployment for Developer Efficiency at WhatsApp
by: Mao, Ke, et al.
Published: (2025)
by: Mao, Ke, et al.
Published: (2025)
ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
by: Lu, Pengrui, et al.
Published: (2026)
by: Lu, Pengrui, et al.
Published: (2026)
Similar Items
-
Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
by: Kashif, Syed Mohammad, et al.
Published: (2026) -
A Survey of Bugs in AI-Generated Code
by: Gao, Ruofan, et al.
Published: (2025) -
An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
by: Yu, Jiaxin, et al.
Published: (2024) -
FasterPy: An LLM-based Code Execution Efficiency Optimization Framework
by: Wu, Yue, et al.
Published: (2025) -
On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
by: Jahromi, Ali Soltanian Fard, et al.
Published: (2026)