.png){kind=icon}
返回
Graph collaborative filtering-based bug triaging☆
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2023.111667.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Issue tracking systems are widely used for collecting bug reports. A target of intelligent software engineering is to automate assigning bugs to appropriate developers. Recently, the momentum of artificial intelligence has brought many successful studies that triage bugs by classifying their reports with NLP-based methods. Some studies also try to introduce context information to represent developers. Nevertheless, they take a fundamental assumption that developers and bugs, closely related entities in real-world scenarios, should be modeled independently. To capture the bug-developer correlations in bug triaging activities, we propose a Graph Collaborative filtering-based Bug Triaging framework: (1) bug-developer correlations are modeled as a bipartite graph; (2) natural language processing-based pre-training is implemented on bug reports to initialize bug nodes; (3) spatial-temporal graph convolution strategy is designed to learn the representation of developer nodes; (4) information retrieval-based classifier is proposed to match bugs and developers. Extensive experiments across mainstream datasets show the competence of our GCBT. Moreover, We believe that GCBT could generally benefit the modeling of correlations in other software engineering scenarios. (c) 2023 Elsevier Inc. All rights reserved.
Keyword:
Software reliability engineering
Bug triaging
Deep graph learning
Graph collaborative filtering
期刊
IF:
4.1
论文数:
5.5K
被引数:
8.4K
机构
引用论文
分享收藏Definitive Chemoradiotherapy for Anal Carcinoma: Long-Term Results Based on Consistent Time-to-Event Endpoints.png)
Oncology
OncologyIF0
PREAI
分享收藏SPAN: A self-paced association augmentation and node embedding-based model for software bug classification and assignmentSPAN: 用于软件错误分类和分配的基于自定进度的关联增强和基于节点嵌入的模型
PREAI
分享收藏分享收藏
Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts自动化错误分配: 大规模工业环境中基于集成的机器学习
PREAI
分享收藏
查看更多内容