Log2model:从日志数据推断行为模型

K. S. Luckow, C. Pasareanu
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引用次数: 3

摘要

我们提出了LOG2MODEL,这是一种由工具支持的方法,可以从日志数据中构建行为模型。记录的数据由时间序列组成,这些时间序列编码在离散时间步上观察到的系统状态值。生成的模型是离散时间马尔可夫链,其状态和转换表示日志中记录的值。这些模型包含关键信息,这些信息可以使用现成的模型检查器(如PRISM)对安全性、延迟、吞吐量等方面进行可视化和分析。用户或自动化工具可以进一步使用分析结果来监视和更改系统行为。我们提出了LOG2MODEL的体系结构及其在空域自主操作环境中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Log2model: inferring behavioral models from log data
We present LOG2MODEL, an approach, supported by a tool, that builds behavioral models from log data. The logged data consists of time series encoding the values of the states of a system observed at discrete time steps. The models generated are Discrete-Time Markov Chains with states and transitions representing the values recorded in the log. The models contain key information that can be visualized and analyzed with respect to safety, delays, throughput etc, using off-the-shelf model checkers such as PRISM. The analysis results can be further used by users or automated tools to monitor and alter the system behavior. We present the architecture of LOG2MODEL and its application in the context of autonomous operations in the airspace domain.
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