arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.01508cs.SE
收藏

需求工程中自然语言处理技术选择与评估的实用指南

Practical Guidelines for the Selection and Evaluation of Natural Language Processing Techniques in Requirements Engineering

Mehrdad Sabetzadeh, Chetan Arora

AI总结:

本文为需求工程中自然语言处理技术的选择与评估提供实用指南,涵盖传统NLP、特征机器学习及语言模型方法,旨在帮助新手快速上手。

摘要:

自然语言处理(NLP)现已成为需求自动化的基石。推动NLP在需求工程(RE)中日益普及的一个关键因素是,工业界普遍使用自然语言(NL)来表述需求。NLP技术常用于自动分类需求、提取重要信息(如领域模型和术语表条目),以及执行质量保证任务,例如歧义处理和完整性检查。由于可用的NLP解决方案策略众多,且可同时应用机器学习,因此针对特定RE任务选择合适策略并以经验上严谨的方式评估最终解决方案可能颇具挑战。本章中,我们提出了在RE背景下选择NLP技术及其评估的指南。具体而言,我们讨论了如何在传统NLP、基于特征的机器学习和基于语言模型的方法等不同策略之间进行选择。我们最终希望本章能作为一块垫脚石,帮助NLP4RE的新手快速入门与RE领域最相关的NLP技术。

英文摘要:

Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.

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。

↑