  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.
