CiRA：一个自动检测需求工件中因果关系的工具
CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts
摘要：
需求通常通过使用因果关系来指定预期的系统行为（例如，如果A，则B）。自动提取这些关系支持两个突出的RE用例：自动测试用例推导和需求之间的依赖检测。然而，现有的工具无法以合理的性能从自然语言中提取因果关系。在本文中，我们提出了我们的工具CiRA（因果关系检测的要求工件），这是第一步自动因果关系提取的要求。我们评估CiRA的61个验收标准（因果关系：32;非因果关系：29）描述的德国Corona-Warn-App的功能公开数据集。我们实现了83%的宏观F_1得分，这证实了我们的方法的可行性。
英文摘要：
Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro F_1 score of 83%, which corroborates the feasibility of our approach.
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。
