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An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

机译：关于在大型支付公司应用被动学习的经验报告

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摘要

Passive learning techniques infer graph models on the behavior of a system from large trace logs. The research community has been dedicating great effort in making passive learning techniques more scalable and ready to use by industry. However, there is still a lack of empirical knowledge on the usefulness and applicability of such techniques in large scale real systems. To that aim, we conducted action research over nine months in a large payment company. Throughout this period, we iteratively applied passive learning techniques with the goal of revealing useful information to the development team. In each iteration, we discussed the findings and challenges to the expert developer of the company, and we improved our tools accordingly. In this paper, we present evidence that passive learning can indeed support development teams, a set of lessons we learned during our experience, a proposed guide to facilitate its adoption, and current research challenges.
机译：被动学习技术从大跟踪日志从系统的行为推断图形模型。研究界一直在努力使被动学习技术更加可扩展，并准备由行业使用。然而，仍然缺乏关于大规模真实系统中这些技术的有用性和适用性的实证知识。为此，我们在一家大型支付公司九个月进行了行动研究。在此期间，我们迭代地应用被动学习技术，其目标是向开发团队揭示有用信息。在每次迭代中，我们向公司专家开发商讨论了调查结果和挑战，我们相应地改善了我们的工具。在本文中，我们提出了被动学习可以确实支持开发团队的证据，我们在我们经验期间了解到的一系列课程，这是一个促进其通过和当前研究挑战的拟议指南。

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