一句话：CrUISE-AC 自动分析公开 issue tracker，用 NLP 与 LLM 集成为用户故事生成额外验收标准，专家评估 80–82% 有效。
软件开发后期修复缺陷代价呈指数增长，需求不完整或模糊是主要缺陷来源。论文提出 CrUISE-AC，一个全自动方法，利用 NLP 技术和 LLM 集成模型，分析同一应用领域内公开可访问的 issue tracker，为给定用户故事生成非平凡的额外验收标准。该方法由五个独立专家在两个业务领域评估，结果显示 80–82% 生成的验收标准为用户故事增加了相关需求，表明 issue tracker 蕴含对需求工程有价值的信息。局限在于依赖可访问的输入 issue，且未检查生成标准是否冲突或与其他用户故事的标准重叠。
Costs for resolving software defects increase exponentially in late stages. Incomplete or ambiguous requirements are one of the biggest sources for defects, since stakeholders might not be able to communicate their needs or fail to share their domain specific knowledge. Combined with insufficient developer experience, teams are prone to constructing incorrect or incomplete features. To prevent this, requirements engineering has to explore knowledge sources beyond stakeholder interviews. Publicly accessible issue trackers for systems within the same application domain hold essential information on identified weaknesses, edge cases, and potential error sources, all documented by actual users. Our research aims at (1) identifying, and (2) leveraging such issues to improve an agile requirements artifact known as a “user story”. We present CrUISE-AC (Crowd and User Informed Suggestion Engine for Acceptance Criteria) as a fully automated method that investigates issues and generates non-trivial additional acceptance criteria for a given user story by employing NLP techniques and an ensemble of LLMs. CrUISE-AC was evaluated by five independent experts in two distinct business domains. Our findings suggest that issue trackers hold valuable information pertinent to requirements engineering. Our evaluation shows that 80–82 % of the generated acceptance criteria add relevant requirements to the user stories. Limitations are the dependence on accessible input issues and the fact that we do not check generated criteria for being conflict-free or non-overlapping with criteria from other user stories.
DOI 原文 ·
@inproceedings{paperbot8845,
title = {From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC},
author = {Stefan Schwedt and Thomas Ströder},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00217}
}