A Survey on Automated Log Analysis for Reliability Engineering
| Autor: | Zhuangbin Chen, Tianyi Yang, Pinjia He, Yuxin Su, Michael R. Lyu, Shilin He |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Parsing Source code General Computer Science Event (computing) business.industry Computer science media_common.quotation_subject Scale (chemistry) Logging Volume (computing) 020207 software engineering 02 engineering and technology computer.software_genre Theoretical Computer Science Reliability engineering Software Engineering (cs.SE) Computer Science - Software Engineering Software 020204 information systems 0202 electrical engineering electronic engineering information engineering Software system business computer media_common |
| DOI: | 10.48550/arxiv.2009.07237 |
| Popis: | 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. Comment: accepted by ACM Computing Survey |
| Databáze: | OpenAIRE |
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