  This paper presents a novel approach to enhance the performance of binary
code comment quality classification models through the application of
Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a
dataset comprising 1239 newly generated code-comment pairs, extracted from
various GitHub repositories and open-source projects, has been labelled as
"Useful" or "Not Useful", and integrated into the existing corpus of 9048 pairs
in the C programming language. Employing a cutting-edge Large Language Model
Architecture, the generated dataset demonstrates notable improvements in model
accuracy. Specifically, when incorporated into the Support Vector Machine (SVM)
model, a 6% increase in precision is observed, rising from 0.79 to 0.85.
Additionally, the Artificial Neural Network (ANN) model exhibits a 1.5%
increase in recall, climbing from 0.731 to 0.746. This paper sheds light on the
potential of Generative AI in augmenting code comment quality classification
models. The results affirm the effectiveness of this methodology, indicating
its applicability in broader contexts within software development and quality
assurance domains. The findings underscore the significance of integrating
generative techniques to advance the accuracy and efficacy of machine learning
models in practical software engineering scenarios.
