{"ID":2877902,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18675","arxiv_id":"2508.18675","title":"Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision","abstract":"Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough in this area. However, existing methods that only rely on LLMs remain inadequate in the quality of generated code, offering no guarantees of satisfying practical requirements. They lack a systematic strategy for requirements development and modeling. Recently, LLM-based agents typically possess powerful abilities and play an essential role in facilitating the alignment of LLM outputs with user requirements. In this paper, we envision the first multi-agent framework for reliable code generation based on \\textsc{re}quirements \\textsc{de}velopment and \\textsc{fo}rmalization, named \\textsc{ReDeFo}. This framework incorporates three agents, highlighting their augmentation with knowledge and techniques of formal methods, into the requirements-to-code generation pipeline to strengthen quality assurance. The core of \\textsc{ReDeFo} is the use of formal specifications to bridge the gap between potentially ambiguous natural language requirements and precise executable code. \\textsc{ReDeFo} enables rigorous reasoning about correctness, uncovering hidden bugs, and enforcing critical properties throughout the development process. In general, our framework aims to take a promising step toward realizing the long-standing vision of reliable, auto-generated software.","short_abstract":"Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough in this area. However, existing methods that only rely on LLMs remain inadequate in the quality of generated code, offering no guarantees of...","url_abs":"https://arxiv.org/abs/2508.18675","url_pdf":"https://arxiv.org/pdf/2508.18675v1","authors":"[\"Xu Lu\",\"Weisong Sun\",\"Yiran Zhang\",\"Ming Hu\",\"Cong Tian\",\"Zhi Jin\",\"Yang Liu\"]","published":"2025-08-26T04:45:04Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}