一句话：将数据影响方法用于识别神经代码模型训练语料中的噪声样本。
尽管神经源代码模型被广泛用于软件工程任务，但其质量仍不足以支撑实际应用，原因之一是训练用源代码语料中可能存在噪声。本文把机器学习中的数据影响方法迁移到源代码语料场景，通过评估目标样本与正确样本的相似性来判断该样本是否为噪声。作者在基于分类的代码任务上进行了评估，结果表明数据影响方法能够从神经代码模型中识别出噪声样本。该工作从以数据为中心的视角出发，为构建更可靠的神经源代码模型提供了新思路，并有望推动更实用的源代码模型的发展。
Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train such models. We adapt data-influence methods to detect such noises in this paper. Data-influence methods are used in machine learning to evaluate the similarity of a target sample to the correct samples in order to determine whether or not the target sample is noisy. Our evaluation results show that data-influence methods can identify noisy samples from neural code models in classification-based tasks. This approach will contribute to the larger vision of developing better neural source code models from a data-centric perspective, which is a key driver for developing useful source code models in practice.
@inproceedings{paperbot7162,
title = {Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora},
author = {Anh T. V. Dau and Nghi D. Q. Bui and Thang Nguyen-Duc and Hoang Thanh-Tung},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3561168}
}