操作日志信息的有效去除：在模型推理中的应用
Effective Removal of Operational Log Messages: an Application to Model Inference
摘要：
模型推理的目的是从软件系统的执行日志中提取准确的模型。然而，在现实中，日志可能包含一些“噪音”，可能会降低模型推理的性能。一种形式的噪声通常可以在系统日志中找到，这些日志不仅包含事务消息-记录系统的功能行为-而且包含操作消息-记录系统的操作状态（例如，用于跟踪存储器使用的周期性心跳）。在低质量的日志中，事务和操作消息随机交错，导致错误地将操作行为包含到系统模型中，理想情况下，系统模型应该只反映系统的功能行为。因此，在推断模型之前删除日志中的操作消息非常重要。在本文中，我们提出了LogCleaner，一种新的技术，用于删除操作日志消息。LogCleaner首先执行周期性分析以过滤掉周期性消息，然后执行依赖性分析以计算所有日志消息的依赖程度，并根据其依赖性删除操作消息。在两个专有数据集和11个公开数据集上的实验结果表明，LogCleaner平均可以准确地删除98%的操作消息，保留81%的事务消息。此外，使用LogCleaner预处理的日志减少了模型推理的执行时间（根据系统的特性，加速范围从1.5到946.7），并通过增加它们接受正确系统行为的能力，显著提高了推断模型的准确性（平均+43.8 pp，pp=百分点）和拒绝不正确的系统行为（平均+15.0 pp）。
英文摘要：
Model inference aims to extract accurate models from the execution logs of software systems. However, in reality, logs may contain some "noise" that could deteriorate the performance of model inference. One form of noise can commonly be found in system logs that contain not only transactional messages---logging the functional behavior of the system---but also operational messages---recording the operational state of the system (e.g., a periodic heartbeat to keep track of the memory usage). In low-quality logs, transactional and operational messages are randomly interleaved, leading to the erroneous inclusion of operational behaviors into a system model, that ideally should only reflect the functional behavior of the system. It is therefore important to remove operational messages in the logs before inferring models. In this paper, we propose LogCleaner, a novel technique for removing operational logs messages. LogCleaner first performs a periodicity analysis to filter out periodic messages, and then it performs a dependency analysis to calculate the degree of dependency for all log messages and to remove operational messages based on their dependencies. The experimental results on two proprietary and 11 publicly available log datasets show that LogCleaner, on average, can accurately remove 98% of the operational messages and preserve 81% of the transactional messages. Furthermore, using logs pre-processed with LogCleaner decreases the execution time of model inference (with a speed-up ranging from 1.5 to 946.7 depending on the characteristics of the system) and significantly improves the accuracy of the inferred models, by increasing their ability to accept correct system behaviors (+43.8 pp on average, with pp=percentage points) and to reject incorrect system behaviors (+15.0 pp on average).
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。
