Context and motivation: Requirements Engineering (RE) researchers have been experimenting Machine Learning (ML) and Deep Learning (DL) approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. Question-problem: Most of today's ML-DL approaches are based on supervised learning techniques, meaning that they need to be trained using annotated datasets to learn how to assign a class label to sample items from an application domain. This constraint poses an enormous challenge to RE researchers, as the lack of annotated datasets makes it difficult for them to fully exploit the benefit of advanced ML-DL technologies. Principal ideas-results: To address this challenge, this paper proposes an approach that employs the embedding-based unsupervised Zero-Shot Learning (ZSL) technique to perform requirements classification. We focus on the classification task because many RE tasks can be framed as classification problems. In this study, we demonstrate our approach for three tasks. (1) FR-NFR: classification functional requirements vs non-functional requirements; (2) NFR: identification of NFR classes; (3) Security: classification of security vs non-security requirements. The study shows that the ZSL approach achieves an F1 score of 0.66 for the FR-NFR task. For the NFR task, the approach yields F1 ~ 0.72-0.80, considering the most frequent classes. For the Security task, F1 ~ 0.66. All of the aforementioned F1 scores are achieved with zero-training efforts. Contribution: This study demonstrates the potential of ZSL for requirements classification. An important implication is that it is possible to have very little or no training data to perform multiple tasks. The proposed approach thus contributes to the solution of the longstanding problem of data shortage in RE.
翻译:要求工程(RE)研究人员一直在对要求分类、要求追踪、模糊性检测和建模等一系列RE任务试用机器学习(ML)和深学习(DL)方法。 问题:今天ML-DL方法大多以监督学习技术为基础,这意味着他们需要使用附加说明的数据集接受培训,以学习如何为来自应用域的样本项目指定一个类标签。这一制约因素对RE研究人员构成巨大挑战,因为缺乏附加说明的数据集使他们难以充分利用先进的ML-DL技术。 主要的构想结果:为了应对这一挑战,本文件建议采用基于嵌入的Zero-Shot学习(ZSL)方法进行需求分类。我们注重分类任务,因为许多RE任务可以被标为分类问题。 在这项研究中,我们展示了三种任务的方法。 (1) FR-NFR:对高级N-DL技术的分类要求与非功能要求;(2) NFR:为N-DR1级确定最经常的成绩;(3) 因此,安全分类为FR-SLSL任务进行非等级任务。