从法规到需求：自动化需求推导与解释管道
From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline
AI总结：
针对法规合规性挑战，提出自动化管道，识别法规文档中条款并推导软件需求及解释。评估效果良好，还实现工具Reg2Req，用户研究表明可提升需求理解与行动信心。
摘要：
确保软件符合法规（如GDPR和欧盟AI法案）是重大挑战，需求工程师将复杂法律文本转化为可行软件需求多靠手动且易出错。我们提出自动化法规到需求管道，识别法规文档中含需求条款并推导通用软件需求及解释。评估管道在GDPR和欧盟AI法案条款集上表现良好，还实现工具Reg2Req，用户研究表明其提升需求理解与行动信心。
英文摘要：
Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) poses a significant challenge, as requirements engineers must translate complex legal text into actionable software requirements - a process that remains largely manual and error-prone in practice. We present an automated regulation-to-requirements pipeline that identifies requirement-bearing clauses in regulatory documents and derives system-agnostic software requirements, accompanied by plain-language explanations, traceable to their legal sources. We evaluate the pipeline on the full clause sets of the GDPR (398 clauses) and the EU AI Act (574 clauses). For requirement-bearing clause identification, the approach achieves macro-averaged F1 scores of 0.82 and 0.78, respectively, outperforming a SetFit-based baseline. Human evaluation shows high completeness (4.60 and 4.45) and correctness (3.74 and 3.54) of derived requirements, while explanation clarity scores are near-ceiling (4.92 and 4.94) on a 1-5 scale. We implement the approach in Reg2Req, a publicly released tool that further supports requirement classification, use case seeding, cross-reference analysis, definition indexing, and a traceability matrix to operationalize regulatory compliance in practice. A user study with 25 practitioners shows that the plain-language explanations significantly improve comprehension of derived requirements and confidence in acting on them (p < 0.001), and that all participants would use Reg2Req as a starting point for deriving software requirements from a regulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
