arXiv:2606.17514v1 Announce Type: cross Abstract: Large Language Models have shown remarkable capabilities in code generation. However, most existing evaluations focus only on single-attempt accuracy and overlook the iterative refinement process that is central to real-world programming. This study presents a systematic investigation of LLMs' ability to rectify their own code through execution feedback. Using real-world programming problems across four models and two major programming languages, this study evaluates performance using iterative refinement framework where LLMs receive compiler e
The rapid advancement in Large Language Models necessitates exploring their practical application beyond initial generation, particularly in robust software development workflows.
This study addresses a critical gap in understanding LLM capabilities for iterative code improvement, indicating a path towards more reliable and autonomous code generation systems.
LLMs are moving beyond 'single-attempt' code generation towards self-correcting and more resilient programming agents, fundamentally altering how software development might be assisted or automated.
- · Software developers
- · AI companies
- · Platform-as-a-Service providers
- · Cloud infrastructure
- · Manual debugging services
- · Legacy code generation tools
Increased efficiency and accuracy in software development via AI-powered iterative correction.
Automation of more complex coding tasks, potentially reducing the development cycle for new applications.
A shift towards 'AI-first' software development paradigms where autonomous agents handle much of the coding and debugging.
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Read at arXiv cs.AI