Context: Requirements engineering researchers have been experimenting with machine learning and deep learning approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. However, most of today's ML/DL approaches are based on supervised learning techniques, meaning that they need to be trained using a large amount of task-specific labelled training data. This constraint poses an enormous challenge to RE researchers, as the lack of labelled data makes it difficult for them to fully exploit the benefit of advanced ML/DL technologies. Objective: This paper addresses this problem by showing how a zero-shot learning approach can be used for requirements classification without using any labelled training data. We focus on the classification task because many RE tasks can be framed as classification problems. Method: The ZSL approach used in our study employs contextual word-embeddings and transformer-based language models. We demonstrate this approach through a series of experiments to perform three classification 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. Results: 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. Conclusion: 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 classification tasks. The proposed approach thus contributes to the solution of the long-standing problem of data shortage in RE.
System- and Software-level Architecting Harmonization Practices for Systems-of-Systems -- An exploratory case study on a long-running large-scale scientific instrument
BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
University of Padova（帕多瓦大学）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；Lero, the Research Ireland Centre for Software, University of Limerick（利默里克大学软件研究爱尔兰中心 Lero）；University of Manchester（曼彻斯特大学）；University College Dublin (UCD)（都柏林大学学院）；Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会信息科学与技术研究所 ISTI）
