文献类型：期刊文献

中文题名：面向智能计算框架的即时缺陷预测

英文题名：Just-In-Time Defect Prediction for Intellignet Computing Frameworks

作者：Ge, Jian[1]; Yu, Hui-Qun[1]; Fan, Gui-Sheng[1]; Tang, Jian-Hao[1]; Huang, Zi-Jie[1]

机构：[1] School of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

卷号：34

期号：9

外文期刊名：Ruan Jian Xue Bao/Journal of Software

收录：EI(收录号：20232414214183)

语种：中文

外文关键词：Computation theory - Computer software selection and evaluation - Forecasting - Intelligent computing - Intelligent systems - Iterative methods - Just in time production - Semantics - Software design

摘要：In recent years, Intelligent Computing frameworks have been widely applied as Artificial Intelligence (AI) engineering implementation tools, and the reliability of Intelligent Computing framework is a key factor to the effectiveness of AI implementation. However, the reliability assurance of the Intelligent Computing Framework is challenging. On the one hand, the code iteration of Intelligent Computing Framework is fast, and testing such code is difficult. On the other hand, unlike traditional software, Intelligent Computing Framework involves a large number of tensor calculations, and its code specification lacks the guidance of software engineering theory. Existing research mostly employs fuzzy testing to localize defects in order to address this issue. However, such method can only accurately discover specific types of faults, and it is difficult to guide developers and let them focus on software quality in the development process. Therefore, this paper predicts the defects of the Intelligent Computing Framework at the code commit level. We use popular Intelligent Computing frameworks (Tensorflow, Baidu PaddlePaddle, etc.) and build a variety of commit-level features to construct datasets. Furthermore, we use LDA to mine code and commit semantic information as new features, and then use Random Forest as a classifier to perform Just-In-Time defect prediction, Results show that the average AUC-ROC performance of 0.77, and introducing semantic features slightly increases the performance. Finally, we use a machine learning model explanation technique called SHAP to analyze the influence of each feature on the prediction output of the model. We discover that (1) the influence of basic features on the model conforms to the characteristics of traditional software development, (2) code and commit semantic features are important in Just-In-Time Defect Prediction of Intelligent Computing frameworks, and (3) the contribution of different features in different systems to the output of the prediction model is also quite different. ? 2023 Chinese Academy of Sciences. All rights reserved.