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

Rohith Arumugam S, Angel Deborah S
10/14/2023
cs.SEcs.AIcs.LG

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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.

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Cite this paper

@article{s2023enhancing,
  title  = {Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy},
  author = {Rohith Arumugam S and Angel Deborah S},
  year   = {2023},
  eprint = {2310.11467},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.11467v1}
}

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