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

arXiv: https://arxiv.org/abs/2310.11467 · HTML: https://mlsanity.com/paper/2310.11467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation](https://mlsanity.com/paper/1610.02583/) (2016)
- [Speculative Analysis for Quality Assessment of Code Comments](https://mlsanity.com/paper/2102.09605/) (2021)

## Abstract (arXiv)

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.