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Zero-Shot Learning for Requirements Classification: An Exploratory Study by Alhoshan, Waad; Ferrari, Alessio; Zhao, Liping is a scholarly article available to read on EtoBox.

What is Zero-Shot Learning for Requirements Classification: An Exploratory Study about?

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: identific

Author

Alhoshan, Waad; Ferrari, Alessio; Zhao, Liping

Published

2023

Language

EN