2023/10/14 by Rohith Arumugam S, S, Rohith Arumugam, Angel Deborah S +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research #Text Readability and Simplification
paper · pdf · doi:10.48550/arxiv.2310.11467
openalex publication_date 2023/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.