arXiv (OAI Expanded)
open access
Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension
Since the advent of Large Language Models (LLMs), practitioners have increasingly leveraged them to support their software engineering tasks, including automated code repair, showing promising results. Yet, concerns regarding reproducibility and generalizability remain largely unexplored. To further
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension#819814
- Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension#627762
- A Survey on Large Language Models for Code Generation#1135850
- When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair#303019
- Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance#821191
Record · ID 809818
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive