摘要
软件缺陷的高效自动分派是保障开源软件质量的重要手段。已有研究多基于机器学习技术,从缺陷报告的文本内容和开发者之间的关系入手,研究软件缺陷的自动分派,而对缺陷报告之间的相关关系和基于深度学习技术的缺陷自动分派关注甚少。针对此问题,本文提出了一种基于图卷积神经网络的开发者推荐方法。该方法利用带权重的余弦相似度构建缺陷报告网络,再在增量学习方法下训练图卷积神经网络模型用于开发者推荐。将近年提出的3种方法设置为实验对照组,在大型开源软件项目Eclipse和Mozilla数据集平台上分别进行实验,结果表明本文提出的方法平均推荐准确率比其他3种方法最高提升了60%和70%左右。
Efficient automatic bug triage is an important means to guarantee the quality of open-source software.Most of the existing researches are based on the machine learning approach to study the bug triaging from the relationship between the text content of the bug reports and the relationship between developers.However,there is little attention to the correlation between bug reports and the deep learning based bug triaging method.Aiming at the problem of automatic bug triage,this paper proposes a developer recommendation method based on the graph convolutional neural network(GCN).Firstly,the bug reports network is constructed by using the cosine similarity with weights,and then the GCN network model is trained for developer recommendation under the incremental learning method.In this paper,three methods proposed in recent years are set as experimental control groups.Experiments are carried out on the large-scale open-source software project Eclipse and Mozilla datasets.The results show that the average recommended accuracy of the proposed method is about 60%and 70%higher than that of the other three methods.
作者
李元香
董夏磊
项正龙
喻飞
吴泓润
LI Yuanxiang;DONG Xialei;XIANG Zhenglong;YU Fei;WU Hongrun(School of Computer Science,Wuhan University,Wuhan 430072,Hubei,China;School of Physics and Information Engineering,Minnan Normal University,Zhangzhou 363000,Fujian,China)
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2020年第3期244-252,共9页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金(61672391)
福建省高校重大教育教学改革研究项目(FBJG20180015)。
关键词
缺陷分派
图卷积神经网络
开发者推荐
开源软件
bug triage
graph convolutional neural network(GCN)
developer recommendation
open-source software
