[论文解读] Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study

Liping Zhao, Waad Alhoshan+5 more|arXiv (Cornell University)|Apr 2, 2020

Software Engineering Techniques and Practices被引用 37

一句话总结

一项系统映射研究综述 NLP4RE 研究，概述研究现状、方法、工具以及跨 404 项原始研究的未解决问题。

ABSTRACT

Natural language processing supported requirements engineering is an area of research and development that seeks to apply NLP techniques, tools and resources to a variety of requirements documents or artifacts to support a range of linguistic analysis tasks performed at various RE phases. Such tasks include detecting language issues, identifying key domain concepts and establishing traceability links between requirements. This article surveys the landscape of NLP4RE research to understand the state of the art and identify open problems. The systematic mapping study approach is used to conduct this survey, which identified 404 relevant primary studies and reviewed them according to five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of the practice, and the NLP technologies used. Results: 1) NLP4RE is an active and thriving research area in RE that has amassed a large number of publications and attracted widespread attention from diverse communities; 2) most NLP4RE studies are solution proposals having only been evaluated using a laboratory experiment or an example application; 3) most studies have focused on the analysis phase, with detection as their central linguistic analysis task and requirements specification as their commonly processed document type; 4) 130 new tools have been proposed to support a range of linguistic analysis tasks, but there is little evidence of adoption in the long term, although some industrial applications have been published; 5) 140 NLP techniques, 66 NLP tools and 25 NLP resources are extracted from the selected studies.

研究动机与目标

• 评估 NLP4RE 研究在文献与实践中的现状。
• 识别在 RE 中使用的常见 NLP 任务和文档类型。
• 评估实证验证实践以及 NLP4RE 在行业中的采用情况。
• 编目 NLP4RE 中使用的 NLP 技术、工具和资源。
• 识别 NLP4RE 的开放问题和未来研究方向。

提出的方法

• 对 NLP4RE 文献进行系统映射研究。
• 按研究问题、NLP 任务、文档类型和实证方法对研究进行分类。
• 从所选研究中提取并统计 NLP 技术、工具和资源。
• 在五个方面综合研究发现：文献现状、实证研究、研究重点、实践现状以及所使用的技术。
• 提供基于证据的开放问题和行业采用情况综述。

实验结果

研究问题

• RQ1 在文献中，NLP4RE 研究的总体状态和分布如何？
• RQ2 哪些实证方法用于评估 NLP4RE 方法？
• RQ3 在 NLP4RE 中研究的主要 NLP 任务和 RE 文档类型有哪些？
• RQ4 在 NLP4RE 中提出或使用了哪些工具、技术和资源，以及它们在实践中被采用的程度？
• RQ5 NLP4RE 研究体系中出现了哪些开放问题和未来方向？

主要发现

• NLP4RE 是一个活跃的领域，发表物众多，社区参与广泛。
• 大多数研究是在实验室或示例应用中评估的解决方案提案，而不是在真实世界部署。
• 分析阶段是主要关注点，检测作为核心语言任务，需求规格说明是常见的文档类型。
• 为各种语言任务提出了 130 种新工具，但长期采用证据有限，已有些工业应用被报道。
• 该综述从所选研究中提取了 140 种 NLP 技术、66 种 NLP 工具和 25 种 NLP 资源。