使用GGNN推荐日志语句级别
Using GGNN to recommend log statement level
摘要：
在软件工程中，由于程序员无法访问用户的程序，只能依靠日志信息来查找错误的根源，因此日志语句是一个重要的组成部分。“日志级别”机制允许开发人员和用户指定在软件执行期间打印的适当数量的日志。而26%的日志语句修改是修改级别。我们尝试使用ML方法来预测日志语句的合适级别。具体模型是GGNN（门控图神经网络），我们借鉴了微软的研究。在这项工作中，我们应用图神经网络来预测一些开源Java项目的日志语句级别的使用情况。鉴于GGNN在此任务中的良好性能，我们相信GGNN是处理源代码的绝佳选择。我们设想该模型可以在更广泛地将AI/ML技术应用于软件开发生存周期方面发挥重要作用。
英文摘要：
In software engineering, log statement is an important part because programmers can't access to users' program and they can only rely on log message to find the root of bugs. The mechanism of "log level" allows developers and users to specify the appropriate amount of logs to print during the execution of the software. And 26\% of the log statement modification is to modify the level. We tried to use ML method to predict the suitable level of log statement. The specific model is GGNN(gated graph neural network) and we have drawn lessons from Microsoft's research. In this work, we apply Graph Neural Networks to predict the usage of log statement level of some open source java projects from github. Given the good performance of GGNN in this task, we are confident that GGNN is an excellent choice for processing source code. We envision this model can play an important role in applying AI/ML technique for Software Development Life Cycle more broadly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
