文献类型：会议论文

英文题名：A novel software defect prediction method based on hierarchical neural network

作者：Yu, Huiqun[1];Sun, Xingjie[2];Zhou, Ziyi[2];Fan, Guisheng[1]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Comp Software Evaluating & Testi, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC)

会议日期：JUL 12-16, 2021

会议地点：ELECTR NETWORK

语种：英文

外文关键词：software defect prediction; abstract syntax tree; hierarchical neural network

摘要：To ensure software reliability, software defect prediction (SDP) techniques are employed to help developers effectively allocate the testing resources. Recently, researchers utilized deep learning models to extract semantic features from abstract syntax tree (AST) of source code which showed a better prediction performance over metric-based methods. However, the existing file-level SDP models representing the AST as a flattened sequence could jeopardize the preservation of long-term dependency. In this paper, we propose a new Defect Prediction framework based on the Hierarchical Neural Network (DP-HNN). Our method makes use of the hierarchical structure of AST by splitting the large file-level AST into several subtrees according to certain AST nodes crucial to SDP task. These subtrees represented by node-level sequences are encoded separately and then serve as the elements of the subtree-level sequence. Finally, a multigranularity fusion approach is performed in the subtree-level encoder to obtain the crucial features that represent the code file. Our proposed DP-HNN is aimed at capturing long-term dependency while preserving fine-grained local information. We conducted experiments on 11 open-source projects considering the cross-version and the mixed-version scenario of within-project SDP. Results show that on average, DP-HNN improves the stateof-the-art method by 14% and 3% on MCC and AUC scores respectively.