We investigate the use of Natural Language Inference (NLI) in automating
requirements engineering tasks. In particular, we focus on three tasks:
requirements classification, identification of requirements specification
defects, and detection of conflicts in stakeholders' requirements. While
previous research has demonstrated significant benefit in using NLI as a
universal method for a broad spectrum of natural language processing tasks,
these advantages have not been investigated within the context of software
requirements engineering. Therefore, we design experiments to evaluate the use
of NLI in requirements analysis. We compare the performance of NLI with a
spectrum of approaches, including prompt-based models, conventional transfer
learning, Large Language Models (LLMs)-powered chatbot models, and
probabilistic models. Through experiments conducted under various learning
settings including conventional learning and zero-shot, we demonstrate
conclusively that our NLI method surpasses classical NLP methods as well as
other LLMs-based and chatbot models in the analysis of requirements
specifications. Additionally, we share lessons learned characterizing the
learning settings that make NLI a suitable approach for automating requirements
engineering tasks.

我们研究了自然语言推理 (NLI) 在自动化需求工程任务中的应用。我们集中在需求分类、需求规范缺陷的识别以及利益相关者需求冲突的检测三个任务。通过在不同学习设置下进行的实验，我们明确证明了我们的 NLI 方法在需求规范分析方面超越了传统的 NLP 方法以及基于大型语言模型和聊天机器人模型的其他方法。此外，我们分享了在学习设置方面的经验教训，使 NLI 成为自动化需求工程任务的合适方法。