计算机应用与软件2026，Vol.43Issue(2)：110-117,8.DOI:10.3969/j.issn.1000-386x.2026.02.015
L3R:基于图神经网络的日志语句级别推荐方法研究
L3R:LOG PRINTING STATEMENT LEVEL RECOMMENDER BASED ON GRAPH NEURAL NETWORK
赤坂居纱美 1张晨曦 1彭鑫1
作者信息
- 1. 复旦大学软件学院 上海 200438||上海市数据科学重点实验室 上海 200438
- 折叠
摘要
Abstract
Due to the lack of a rigorous specification to guide logging behaviors,choosing the correct level for log statements is a challenge.Prior studies on log level suggestion ignore the relationship between statements and fail to provide suggestions for logging statements at any specific positions.Based on this,L3R,a GNN-based log level suggest method,is proposed.The method took statement features as nodes,control flow and data flow edges as edges to construct a context graph,updated the logging statement feature based on the relational graph attention network and implemented the log level prediction.Evaluations were conducted on 7 open-source projects,which verified the effectiveness of the method.关键词
日志/日志增强/日志级别建议/图神经网络Key words
Logs/Log enhancement/Log level suggestion/Graph neural network分类
信息技术与安全科学引用本文复制引用
赤坂居纱美,张晨曦,彭鑫..L3R:基于图神经网络的日志语句级别推荐方法研究[J].计算机应用与软件,2026,43(2):110-117,8.
