# From Defects to Demands: A Unified, Iterative, and Heuristically Guided LLM-Based Framework for Automated Software Repair and Requirement Realization

arXiv: https://arxiv.org/abs/2412.05098 · HTML: https://mlsanity.com/paper/2412.05098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)

## Built on by (most cited first, 1 of 1)

- [Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification](https://mlsanity.com/paper/2511.00202/) (2025)

## Abstract (arXiv)

This manuscript signals a new era in the integration of artificial intelligence with software engineering, placing machines at the pinnacle of coding capability. We present a formalized, iterative methodology proving that AI can fully replace human programmers in all aspects of code creation and refinement. Our approach, combining large language models with formal verification, test-driven development, and incremental architectural guidance, achieves a 38.6% improvement over the current top performer's 48.33% accuracy on the SWE-bench benchmark. This surpasses previously assumed limits, signaling the end of human-exclusive coding and the rise of autonomous AI-driven software innovation. More than a technical advance, our work challenges centuries-old assumptions about human creativity. We provide robust evidence of AI superiority, demonstrating tangible gains in practical engineering contexts and laying the foundation for a future in which computational creativity outpaces human ingenuity.