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Logging is a development practice that plays an important role in theoperations and monitoring of complex systems. Developers place log statementsin the source code and use log data to understand how the system behaves inproduction. Unfortunately, anticipating where to log during development ischallenging. Previous studies show the feasibility of leveraging machinelearning to recommend log placement despite the data imbalance since logging isa fraction of the overall code base. However, it remains unknown how thosetechniques apply to an industry setting, and little is known about the effectof imbalanced data and sampling techniques.  In this paper, we study the log placement problem in the code base of Adyen,a large-scale payment company. We analyze 34,526 Java files and 309,527 methodsthat sum up +2M SLOC. We systematically measure the effectiveness of fivemodels based on code metrics, explore the effect of sampling techniques,understand which features models consider to be relevant for the prediction,and evaluate whether we can exploit 388,086 methods from 29 Apache projects tolearn where to log in an industry setting.  Our best performing model achieves 79% of balanced accuracy, 81% ofprecision, 60% of recall. While sampling techniques improve recall, theypenalize precision at a prohibitive cost. Experiments with open-source datayield under-performing models over Adyen's test set; nevertheless, they areuseful due to their low rate of false positives. Our supporting scripts andtools are available to the community.

An Exploratory Study of Log Placement Recommendation in an Enterprise System