论文标题
用于机器学习的软件记录
Software Logging for Machine Learning
论文作者
论文摘要
系统日志在软件密集型系统中执行关键功能，因为日志记录系统的状态以及在重要时间点系统中的重要事件。不幸的是，日志条目通常以临时，非结构化和不协调的方式创建，从而限制了它们对分析和机器学习的有用性。在本文中，我们提出了基于世界领先的电信公司的深入案例研究的大型，复杂，软件密集型系统生成和存储系统日志数据的当代方法的主要挑战。其次，我们提出了一种系统和结构化的方法，用于生成未遭受上述挑战的日志数据，并且已在机器学习中进行了优化。第三，我们根据专家访谈确认该方法解决了确定的挑战和问题，我们提供了该方法的验证。
System logs perform a critical function in software-intensive systems as logs record the state of the system and significant events in the system at important points in time. Unfortunately, log entries are typically created in an ad-hoc, unstructured and uncoordinated fashion, limiting their usefulness for analytics and machine learning. In this paper, we present the main challenges of contemporary approaches to generating and storing system logs data for large, complex, software-intensive systems based on an in-depth case study at a world-leading telecommunications company. Second, we present a systematic and structured approach for generating log data that does not suffer from the aforementioned challenges and is optimized for use in machine learning. Third, we provide validation of the approach based on expert interviews that confirms that the approach addresses the identified challenges and problems.
