一句话：结合 WFST 与可变特征向量 SVM，从交互记录和项目文档中动态抽取并分类需求相关知识，并以三个案例研究验证其有效性。
在跨领域软件开发中，分析师需要从与利益相关者的交互以及既有项目文档中及时、准确地获取并分类需求相关知识，但领域不熟悉会降低这一过程的效率与准确性。本文将问题建模为从实时交互记录与项目文档中挖掘与当前讨论需求主题相关的短语单元，并同时使用生成式与判别式方法：利用加权有限状态转换器（WFST）灵活地对自然语言处理任务进行动态建模以抽取相关术语，并采用支持可变长度特征向量的扩展支持向量机（SVM）实现动态的抽取与分类。作者在三个案例研究中用编辑距离与精确率/召回率进行定性与定量评估，结果表明抽取出的片段在直觉上相关且准确度合理；同时还发现平滑方法、词语邻接性与词序等统计和语言参数会影响抽取与分类的性能。
In many software development projects, analysts are required to deal with systems' requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.
@inproceedings{AbadGZF19,
title = {Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge},
author = {Zahra Shakeri Hossein Abad and Vincenzo Gervasi and Didar Zowghi and Behrouz H. Far},
booktitle = {ICSE},
year = {2019},
doi = {10.1109/icse.2019.00057}
}