PRINS：面向基于规则的系统推理的可扩展模型推理
PRINS: Scalable Model Inference for Component-based System Logs
摘要：
行为软件模型在许多软件工程任务中起着关键作用;不幸的是，这些模型要么在软件开发过程中不可用，要么随着实现的发展而迅速过时。模型推理技术已被提出作为一个可行的解决方案，从执行日志中提取有限状态模型。然而，现有技术在处理在实践中常见的非常大的日志时不能很好地扩展。在本文中，我们解决的可扩展性问题，推断模型的基于组件的系统从大型系统日志，而不需要任何额外的信息。我们的模型推理技术，称为PRINS，遵循分而治之的方法。其思想是首先从相应的日志中推断每个系统组件的模型;然后，考虑到日志中反映的跨组件的事件流，将各个组件模型合并在一起。我们评估PRINS的可扩展性和准确性，使用九个数据集组成的日志提取公开可用的基准和个人电脑运行桌面业务应用程序。结果表明，PRINS可以处理大型日志比公开可用的和众所周知的最先进的工具，而不会显着损害推断模型的准确性。
英文摘要：
Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite state models from execution logs. However, existing techniques do not scale well when processing very large logs that can be commonly found in practice. In this paper, we address the scalability problem of inferring the model of a component-based system from large system logs, without requiring any extra information. Our model inference technique, called PRINS, follows a divide-and-conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the flow of events across components, as reflected in the logs. We evaluated PRINS in terms of scalability and accuracy, using nine datasets composed of logs extracted from publicly available benchmarks and a personal computer running desktop business applications. The results show that PRINS can process large logs much faster than a publicly available and well-known state-of-the-art tool, without significantly compromising the accuracy of inferred models.
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。
