现代电子技术2024，Vol.47Issue(20)：101-108,8.DOI:10.16652/j.issn.1004-373x.2024.20.016
面向可解释性的软件缺陷预测主动学习方法
Interpretability-oriented active learning approach for software defect prediction
摘要
Abstract
In allusion to the problems of high cost of data annotation and lack of interpretability of deep learning model in software defect prediction,an interpretability-oriented active learning approach for software defect prediction is proposed.Based on the active learning technology,samples with high uncertainty are filtered from the target project by means of sample selection strategy for expert annotation,and these annotated samples are put into the source project to train the predictor.The selected samples are perturbed by means of domain knowledge to construct a local dataset,and the behavior of the data selection strategy is simulated on this dataset by means of the linear model to achieve the interpretability of the model.The experimental results show that this approach has better performance than the traditional active learning benchmark approach in data annotation.Meanwhile,the RMSE metrics of the method are also lower than those of LIME,Global Agent Model,and RuleFit in terms of interpretability,which can better explain the black-box model.This approach can not only effectively improve the annotation efficiency of software defect data,but also achieve the interpretability of the model.关键词
软件缺陷预测/主动学习/可解释性/数据标注/数据选择策略/深度学习Key words
software defect prediction/active learning/interpretability/data annotation/data selection strategy/deep learning分类
信息技术与安全科学引用本文复制引用
王越,李勇,张文静..面向可解释性的软件缺陷预测主动学习方法[J].现代电子技术,2024,47(20):101-108,8.基金项目
新疆自治区自然科学基金项目(2022D01A225) （2022D01A225）
新疆自治区重点研发计划项目(2022B01007-1) （2022B01007-1）
