  Transformer-based neural networks have demonstrated remarkable performance in
natural language processing tasks such as sentiment analysis. Nevertheless, the
issue of ensuring the dependability of these complicated architectures through
comprehensive testing is still open. This paper presents a collection of
coverage criteria specifically designed to assess test suites created for
transformer-based sentiment analysis networks. Our approach utilizes input
space partitioning, a black-box method, by considering emotionally relevant
linguistic features such as verbs, adjectives, adverbs, and nouns. In order to
effectively produce test cases that encompass a wide range of emotional
elements, we utilize the k-projection coverage metric. This metric minimizes
the complexity of the problem by examining subsets of k features at the same
time, hence reducing dimensionality. Large language models are employed to
generate sentences that display specific combinations of emotional features.
The findings from experiments obtained from a sentiment analysis dataset
illustrate that our criteria and generated tests have led to an average
increase of 16\% in test coverage. In addition, there is a corresponding
average decrease of 6.5\% in model accuracy, showing the ability to identify
vulnerabilities. Our work provides a foundation for improving the dependability
of transformer-based sentiment analysis systems through comprehensive test
evaluation.
