公开(公告)号：US20250013463A1

公开(公告)日：2025-01-09

申请号：US18650290

申请日：2024-04-30

Applicant: Huazhong University of Science and Technology

Inventor： Zhen LI ,  Junyao YE ,  Deqing ZOU ,  Hai JIN ,  Xianghong ZENG

IPC: G06F8/73 ,  G06V10/764

Abstract: A method, system and processor for enhancing robustness of a source-code classification model based on invariant features is provided, wherein the method includes: combining non-robustness features to generate different style templates, converting codes in an input code training set into new codes of different styles to obtain a converted-code training set, merging the input-code and the converted-code training set into an expanded training set, and converting code texts in the expanded training set into code images; and converting the code images into required vectors, pairing samples of identical class randomly picked from the expanded training set and inputting the matched sample pairs into a feature extractor, iteratively updating the feature extractor and the matched sample pairs and extracting target characteristics, and training the extracted invariant features in a classifier to produce a trained model. The disclosed system includes a training set-expanding module and a model-training module.