Hybrid defect prediction model based on network representation learning

doi:10.11772/j.issn.1001-9081.2019061028

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (12): 3633-3638.DOI: 10.11772/j.issn.1001-9081.2019061028

• Computer software technology • Previous Articles Next Articles

Hybrid defect prediction model based on network representation learning

LIU Chengbin^1,2, ZHENG Wei^1,2, FAN Xin^1,2, YANG Fengyu^1,2

1. School of Software, Nanchang Hangkong University, Nanchang Jiangxi 330063, China;
2. Software Evaluation Center, Nanchang Hangkong University, Nanchang Jiangxi 330063, China

Received:2019-06-18 Revised:2019-09-16 Online:2019-12-10 Published:2019-09-29
Contact: 郑巍
Supported by:
This work is partially supported by the Natural Science Foundation of Jiangxi Provincial Department of Education (GJJ180523).

基于网络表征学习的混合缺陷预测模型

刘成斌^1,2, 郑巍^1,2, 樊鑫^1,2, 杨丰玉^1,2

1. 南昌航空大学软件学院, 南昌 330063;
2. 南昌航空大学软件测评中心, 南昌 330063

作者简介:刘成斌(1994-),男,江西吉安人,硕士研究生,CCF会员,主要研究方向:软件缺陷预测、网络表示学习;郑巍(1982-),男,江西萍乡人,副教授,博士,CCF会员,主要研究方向:软件测试、网络优化;樊鑫(1981-),男,湖北荆州人,讲师,硕士,CCF会员,主要研究方向:软件测试自动化、软件缺陷预测;杨丰玉(1980-),男,江西九江人,副教授,硕士,CCF会员,主要研究方向:软件工程、数据挖掘。
基金资助:
江西省教育厅自然科学基金资助项目（GJJ180523）。

Abstract

Abstract: Aiming at the problem of the dependence between software system modules, a hybrid defect prediction model based on network representation learning was constructed by analyzing the network structure of software system. Firstly, the software system was converted into a software network on a module-by-module basis. Then, network representation technique was used to perform the unsupervised learning on the system structural feature of each module in software network. Finally, the system structural features and the semantic features learned by the convolutional neural network were combined to construct a hybrid defect prediction model. The experimental results show that the hybrid defect prediction model has better defect prediction effects in three open source softwares, poi, lucene and synapse of Apache, and its F1 index is respectively 3.8%, 1.0%, 4.1% higher than that of the optimal model based on Convolutional Neural Network (CNN). Software network structure feature analysis provides an effective research thought for the construction of defect prediction model.

Key words: software network, defect prediction, Convolutional Neural Network (CNN), semantic feature, network representation learning

摘要： 针对软件系统模块间具有依赖关系的问题，通过对软件系统网络结构进行分析，构建了基于网络表征学习的混合缺陷预测模型。首先，将软件系统以模块为单位转换成软件网络；然后，使用网络表征技术来无监督学习软件网络中每个模块的系统结构特征；最后，结合系统结构特征和卷积神经网络学习的语义特征构建一个混合缺陷预测模型。实验结果表明：在Apache三个开源软件poi、lucene和synapse上所提混合缺陷预测模型具有更好的缺陷预测效果，其F1指标比最优模型——基于卷积神经网络（CNN）的缺陷预测模型分别提高了3.8%、1.0%、4.1%。软件网络结构特征分析为缺陷预测模型的构建提供了有效的研究思路。

关键词: 软件网络, 缺陷预测, 卷积神经网络, 语义特征, 网络表征学习

CLC Number:

TP311.53

LIU Chengbin, ZHENG Wei, FAN Xin, YANG Fengyu. Hybrid defect prediction model based on network representation learning[J]. Journal of Computer Applications, 2019, 39(12): 3633-3638.

刘成斌, 郑巍, 樊鑫, 杨丰玉. 基于网络表征学习的混合缺陷预测模型[J]. 计算机应用, 2019, 39(12): 3633-3638.

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Hybrid defect prediction model based on network representation learning

基于网络表征学习的混合缺陷预测模型

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