Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy
Abstract
This report focuses on enhancing a binary code comment quality classification model by integrating generated code and comment pairs, to improve model accuracy. The dataset comprises 9048 pairs of code and comments written in the C programming language, each annotated as "Useful" or "Not Useful." Additionally, code and comment pairs are generated using a Large Language Model Architecture, and these generated pairs are labeled to indicate their utility. The outcome of this effort consists of two classification models: one utilizing the original dataset and another incorporating the augmented dataset with the newly generated code comment pairs and labels.
Cite
@article{arxiv.2310.11467,
title = {Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy},
author = {Rohith Arumugam S and Angel Deborah S},
journal= {arXiv preprint arXiv:2310.11467},
year = {2023}
}
Comments
11 pages, 2 figures, 2 tables, Has been accepted for the Information Retrieval in Software Engineering track at Forum for Information Retrieval Evaluation 2023