arXiv (All)
open access
Large Language Model Guided Self-Debugging Code Generation
Automated code generation is gaining significant importance in intelligent computer programming and system deployment. However, current approaches often face challenges in computational efficiency and lack robust mechanisms for code parsing and error correction. In this work, we propose a novel fram
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
- Pinning Decisions Before Failure: Executable Records of Underspecified Choices in AI-Assisted Code Generation#1187915
- Pinning Decisions Before Failure: Executable Records of Underspecified Choices in AI-Assisted Code Generation#1181702
- Assessing the Promise and Pitfalls of ChatGPT for Automated CS1-Driven Code Generation#1141217
- How effective are traditional test criteria at detecting bugs in large language models generated code?#987077
- If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs#997036
Record · ID 1535989
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