2024/02/25 by Hanna Abi Akl, Akl, Hanna Abi
Computer Science · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2402.16910
openalex publication_date 2024/02/25 · openalex created_date 2024/02/28 · openalex updated_date 2026/08/01
We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, achieves a Macro-F1 score of 91.412% with an increase of 1.033% after data augmentation.