文献类型：期刊文献

英文题名：Software Defect Prediction via Attention-Based Recurrent Neural Network

作者：Fan, Guisheng[1,2];Diao, Xuyang[1];Yu, Huiqun[1];Yang, Kang[1];Chen, Liqiong[3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Key Lab Comp Software Evaluating & Testi, Shanghai 201112, Peoples R China;[3]Shanghai Inst Technol, Dept Comp Sci & Informat Engn, Shanghai 201418, Peoples R China

年份：2019

卷号：2019

外文期刊名：SCIENTIFIC PROGRAMMING

收录：;EI(收录号：20191906896796);WOS:【SCI-EXPANDED(收录号:WOS:000466591600001)】；

基金：This work was partially supported by the NSF of China under Grant nos. 61772200 and 61702334, Shanghai Pujiang Talent Program under grants no. 17PJ1401900, Shanghai Municipal Natural Science Foundation under Grant nos. 17ZR1406900 and 17ZR1429700, Educational Research Fund of ECUST under Grant no. ZH1726108, and the Collaborative Innovation Foundation of Shanghai Institute of Technology under Grant no. XTCX2016-20.

语种：英文

外文关键词：Trees (mathematics) - Program debugging - Defects - Semantics - Software reliability - Syntactics - Forecasting - Open source software

摘要：In order to improve software reliability, software defect prediction is applied to the process of software maintenance to identify potential bugs. Traditional methods of software defect prediction mainly focus on designing static code metrics, which are input into machine learning classifiers to predict defect probabilities of the code. However, the characteristics of these artificial metrics do not contain the syntactic structures and semantic information of programs. Such information is more significant than manual metrics and can provide a more accurate predictive model. In this paper, we propose a framework called defect prediction via attention-based recurrent neural network (DP-ARNN). More specifically, DP-ARNN first parses abstract syntax trees (ASTs) of programs and extracts them as vectors. Then it encodes vectors which are used as inputs of DP-ARNN by dictionary mapping and word embedding. After that, it can automatically learn syntactic and semantic features. Furthermore, it employs the attention mechanism to further generate significant features for accurate defect prediction. To validate our method, we choose seven open-source Java projects in Apache, using F1-measure and area under the curve (AUC) as evaluation criteria. The experimental results show that, in average, DP-ARNN improves the F1-measure by 14% and AUC by 7% compared with the state-of-the-art methods, respectively.