arXiv (Cornell University) · 2020 · 35 citations · 154 references
Software MaintenanceEngineeringSoftware EngineeringSource Code AnalysisSoftware AnalysisReliability EngineeringData ScienceData MiningSoftware Source CodeSystems EngineeringLog ManagementData ManagementSoftware MiningReliabilityAutomated Log AnalysisComputer ScienceStatic Program AnalysisSoftware DesignLog AnalysisSoftware LogsReliability ModellingProgram AnalysisSoftware Testing
Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.
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Distributed Representations of Words and Phrases and their Compositionality
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Natural Language Processing, Sequence Modelling, Anomaly Detection +11
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