一句话：关注从自然语言需求制品中自动抽取因果关系，并论证其可支持测试用例等后续制品的半自动生成。
该文关注需求工程中的因果关系抽取问题。需求制品主要使用自然语言书写，其中的因果句携带了关于需求对象的重要上下文，因此自动检测并抽取因果关系具有应用潜力。摘要进一步指出，抽取并形式化的因果关系可用于半自动翻译为测试用例等其他制品，从而为需求工程中的自动化支持提供动机。然而，给出的摘要仅包含研究背景与动机，未说明具体的抽取方法、模型或算法，也未报告数据集、实验设置与评估结果，因此无法概括其关键结果或性能表现。
Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of requirements engineering is eligible for multiple reasons: (1) requirements artifacts are primarily written in natural language, (2) causal sentences convey essential context about the subject of requirements, and (3) extracted and formalized causality relations are usable for a (semi-)automatic translation into further artifacts, such as test cases.
@inproceedings{paperbot5955,
title = {Automatic extraction of cause-effect-relations from requirements artifacts},
author = {Julian Frattini and Maximilian Junker and Michael Unterkalmsteiner and Daniel Mendez},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3416549}
}