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A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis

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arxiv 2110.12489 v2 pith:I222GTVL submitted 2021-10-24 cs.SE cs.LG

classification cs.SEcs.LG
keywords fieldlogginglogsresearcherssystemanalysisfurtherinformation
verification ladder T0 review T1 audit T2 compute T3 formal
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Logs are widely used to record runtime information of software systems, such as the timestamp and the importance of an event, the unique ID of the source of the log, and a part of the state of a task's execution. The rich information of logs enables system developers (and operators) to monitor the runtime behaviors of their systems and further track down system problems and perform analysis on log data in production settings. However, the prior research on utilizing logs is scattered and that limits the ability of new researchers in this field to quickly get to the speed and hampers currently active researchers to advance this field further. Therefore, this paper surveys and provides a systematic literature review and mapping of the contemporary logging practices and log statements' mining and monitoring techniques and their applications such as in system failure detection and diagnosis. We study a large number of conference and journal papers that appeared on top-level peer-reviewed venues. Additionally, we draw high-level trends of ongoing research and categorize publications into subdivisions. In the end, and based on our holistic observations during this survey, we provide a set of challenges and opportunities that will lead the researchers in academia and industry in moving the field forward.

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  1. Exploring Block Anomaly Detection In HDFS Log Data Analysis

    cs.LG 2026-07 conditional novelty 4.0 of 10

    An LLM-BiLSTM hybrid model detects anomalous HDFS log blocks with 94.8% F1 on the Loghub HDFS dataset, outperforming DeepLog.

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