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This report focuses on enhancing a binary code comment quality classificationmodel by integrating generated code and comment pairs, to improve modelaccuracy. The dataset comprises 9048 pairs of code and comments written in theC programming language, each annotated as "Useful" or "Not Useful."Additionally, code and comment pairs are generated using a Large Language ModelArchitecture, and these generated pairs are labeled to indicate their utility.The outcome of this effort consists of two classification models: one utilizingthe original dataset and another incorporating the augmented dataset with thenewly generated code comment pairs and labels.

Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy