arXiv cs.AI· Yongjian Tang, Costel Moraru, Doruk Tuncel, Hanya Elhashemy, Steffen Klepke, Thomas Runkler·· 6 天前AI 评分24
Agent4Spec：基于 LLM 智能体系统通过自动验证与市场分析优化 API 规范
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis
AI 导读
Agent4Spec 是一个自主多智能体框架，包含验证与市场分析两条流水线，前者检测并自我修正 OpenAPI 语法错误，后者开展竞品研究、识别功能缺口并自动生成缺失的 API 规范功能。在覆盖通用与工业场景的两个 API 规范数据集上，9 个 SOTA LLM 中表现最佳者实现 100% 语法错误检测率和超 95% 修正实施成功率，建议的端点改进采纳率超过 50%。
正文 · 原文
Abstract:API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generation. While recent advancements have leveraged Large Language Models (LLMs) to generate initial specifications from source code, existing approaches treat this process as a one-time event - overlooking the needs for continuous validation, long-term maintenance, and market-driven evolution of these artifacts. To address this issue, we present Agent4Spec, an autonomous multi-agent framework featuring two distinct pipelines: (1) a validation pipeline that detects and self-corrects OpenAPI syntax errors, and (2) a market analysis pipeline that conducts competitive research, identifies functional gaps against industry leaders, and automatically generates missing API-spec functionalities. We evaluate Agent4Spec on two curated datasets of API specifications spanning both general-purpose and industrial scenarios. Among 9 SOTA LLMs, the top-performer achieves 100% syntax error detection rates and over 95% implementation success for proposed corrections, while securing over 50% adoption rates for suggested endpoint improvements, representing a practical tool for API development.
| Comments: | Accepted to FLLM main track |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.13273 [cs.SE] |
| (or arXiv:2610.13273v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13273 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yongjian Tang [view email]
[v1]
Fri, 9 Oct 2026 09:25:30 UTC (728 KB)
来源：arXiv cs.AI · arxiv.org