基于多头自注意力机制的深度缺陷分派模型  

Deep Bug Triage Model Based on Multi-head Self-attention Mechanism

作　　者：万发洋 于旭[1] 徐其江[2] WAN Fa-yang;YU Xu;XU Qi-jiang(College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China;Shandong Vocational College of Information Technology, Weifang 261061, China)

机构地区：[1]青岛科技大学信息科学技术学院,山东青岛266061 [2]山东信息职业技术学院,山东潍坊261061

出　　处：《计算机与现代化》2021年第11期39-43,共5页Computer and Modernization

基　　金：国家自然科学基金资助项目(61402246,61273180,61375067,61773384);山东省自然科学基金资助项目(ZR2019MF014);山东省重点研发计划项目(2018GGX101052)。

摘　　要：当前,缺陷跟踪系统通过缺陷报告实现缺陷与修复者的匹配。然而,以往的缺陷分派模型过于依赖缺陷报告的文本质量,引入自然语言中大量的冗余信息,并忽略了缺陷报告的元字段作为标签属性时存在于修复者之间的社区关系,使得模型结果表现较差。针对以上问题,本文提出一种基于多头自注意力机制的深度缺陷分派模型MSDBT(Multi-head Self-attention Deep Bug Triage)。对缺陷报告的文本内容以及根据元字段生成的修复者序列进行向量化;通过多头自注意力机制在内部的输入元素之间进行并行注意力计算。在4个开源软件项目上的实验结果表明,MSDBT在召回率指标上较之前模型具有明显的优势。At present,bug tracking system realizes the matching of bug and fixer through bug report.However,the previous bug triage model relies too much on the text quality of the bug report,introduces a lot of redundant information in natural language,and ignores the community relationship between the fixers when the meta field of the bug report is used as the label attribute,which makes the model performance worse.Aiming at the above problems,this paper proposes a multi-head self-attention deep bug triage(MSDBT).The text description of the bug report and the fixer sequence generated from meta field are vectorized;the multi-head self-attention mechanism is used to perform parallel attention calculation among the internal input elements.The results of experiments on four open source software projects show that MSDBT has obvious advantages over the previous model in terms of recall index.

关 键 词：缺陷跟踪系统 缺陷分派 深度学习 修复者社区 多头自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]