A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 247 references that are in the map. First posted on arXiv 24 Oct 2021.
Builds on
18 of 247 references.
References · 18
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- 2018Tools and Benchmarks for Automated Log Parsingcited by 56
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6 more
- 2021Studying Duplicate Logging Statements and Their Relationships with Code Clonescited by 7
- 2017Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applicationscited by 6
- 2021An Exploratory Study of Log Placement Recommendation in an Enterprise Systemcited by 4
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Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 4.
6 papers.
- 2025Task-Aware Reduction for Scalable LLM-Database Systemscited by 1
- 2026Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classificationcited by 1
- 2026LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysiscited by 1
- 2023AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarioscited by 0
- 2026TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Modelscited by 0
- 2026Exploring Block Anomaly Detection In HDFS Log Data Analysiscited by 0
- 2026Exploring Block Anomaly Detection In HDFS Log Data Analysis31 Jul 2026
- 2026TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models13 Jul 2026
- 2026LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysis28 Jan 2026
- 2026Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification12 Jan 2026
- 2025Task-Aware Reduction for Scalable LLM-Database Systems13 Oct 2025
- 2023AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios5 Nov 2023