一句话：针对缺陷预测依赖特定特征提取工具与定制分类器的问题，提出融合软件可视化和深度迁移学习的方法。
软件缺陷预测旨在自动定位有缺陷的代码模块，从而更合理地分配测试资源与人力。典型预测流程首先借助外部工具抽取程序特征（如抽象语法树），再使用机器学习分类模型预测缺陷模块。这类方法依赖特定的特征抽取工具，分类器也往往需要针对具体工具与数据定制，限制了有效性与泛化能力。论文标题表明，作者提出软件可视化与深度迁移学习相结合的方法，以改进缺陷预测。摘要未给出具体模型、实验设置与量化结果。
Software defect prediction aims to automatically locate defective code modules to better focus testing resources and human effort. Typically, software defect prediction pipelines are comprised of two parts: the first extracts program features, like abstract syntax trees, by using external tools, and the second applies machine learning-based classification models to those features in order to predict defective modules. Since such approaches depend on specific feature extraction tools, machine learning classifiers have to be custom-tailored to effectively build most accurate models.
DOI 原文 ·
@inproceedings{paperbot5580,
title = {Software visualization and deep transfer learning for effective software defect prediction},
author = {Jinyin Chen and Keke Hu and Yue Yu and Zhuangzhi Chen and Qi Xuan and Yi Liu and Vladimir Filkov},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380389}
}