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P2 Reducing Bug Triaging Confusion by Learning from Mistakes with a Bug Tossing Knowledge Graph

利用缺陷转派知识图谱从错误中学习以降低缺陷分诊混淆
ASE 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)2021匹配度 2/10 · 论文聚焦于缺陷组件分配(bug triaging),属于软件工程/缺陷修复领域,使用学习排序和知识图谱;而用户研究方向是程序语言与AI的交叉,特别是静态分析、形式化验证、浮点/贝叶斯程序分析及其与LLM/Agent的结合。该论文不涉及静态分析、形式化验证、浮点或贝叶斯分析,也未使用LLM/Agent作为工具,因此匹配度很低。引用 19 inbox
Yanqi Su, Zhenchang Xing, Xin Peng, Xin Xia, Chong Wang, Xiwei Xu, Liming Zhu
crossref: 2026-09-12

AI 速览

一句话:提出学习排序框架,利用正确、错误与无关历史缺陷-组件分配及缺陷转派知识图谱,显著提升缺陷组件分配准确率。

将缺陷分配给正确组件是分析和修复缺陷的前提。作者对 Mozilla 124,477 个缺陷的研究发现,错误组件分配频繁且广泛,许多错误重复出现,甚至被 BugBug 工具误导。为此,论文提出一个学习排序框架,从历史中正确、错误和无关的缺陷-组件分配中学习,并构建 Bug Tossing 知识图谱,纳入面向目标的组件转派关系、组件转派社区、组件描述及历史关闭/转派缺陷,导出三类七种特征。在六个 Mozilla 产品、186 个组件、98,587 个关闭缺陷(含 29,100 个转派缺陷)上评估,Top-k 准确率提升超 20%,NDCG@k(k=1,3,5,10)提升超 30%。

问题
缺陷组件分配错误频繁且重复,复杂组件设计、误导性组件名和缺陷报告关键词会混淆报告者、开发者乃至自动分诊工具。
方法
提出学习排序框架,利用历史正确、错误与无关的缺陷-组件分配,并构建 Bug Tossing 知识图谱,提取缺陷、组件及关系三类七种特征进行组件推荐。
评估
在 Mozilla 六个产品、186 个组件、98,587 个关闭缺陷(含 29,100 个转派缺陷)上与 BugBug 及其组件转派关系增强版比较,Top-k 准确率和 NDCG@k 显著提升。
原文摘要(Abstract)

Assigning bugs to the right components is the prerequisite to get the bugs analyzed and fixed. Classification-based techniques have been used in practice for assisting bug component assignments, for example, the BugBug tool developed by Mozilla. However, our study on 124,477 bugs in Mozilla products reveals that erroneous bug component assignments occur frequently and widely. Most errors are repeated errors and some errors are even misled by the BugBug tool. Our study reveals that complex component designs and misleading component names and bug report keywords confuse bug component assignment not only for bug reporters but also developers and even bug triaging tools. In this work, we propose a learning to rank framework that learns to assign components to bugs from correct, erroneous and irrelevant bug-component assignments in the history. To inform the learning, we construct a bug tossing knowledge graph which incorporates not only goal-oriented component tossing relationships but also rich information about component tossing community, component descriptions, and historical closed and tossed bugs, from which three categories and seven types of features for bug, component and bug-component relation can be derived. We evaluate our approach on a dataset of 98,587 closed bugs (including 29,100 tossed bugs) of 186 components in six Mozilla products. Our results show that our approach significantly improves bug component assignments for both tossed and non-tossed bugs over the BugBug tool and the BugBug tool enhanced with component tossing relationships, with >20% Top-k accuracies and >30% NDCG@k (k=1,3,5,10).

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot6479,
  title = {Reducing Bug Triaging Confusion by Learning from Mistakes with a Bug Tossing Knowledge Graph},
  author = {Yanqi Su and Zhenchang Xing and Xin Peng and Xin Xia and Chong Wang and Xiwei Xu and Liming Zhu},
  booktitle = {ASE},
  year = {2021},
  doi = {10.1109/ase51524.2021.9678574}
}