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Logging is a significant programming practice. Due to the highly transactional nature of modern software applications, massive amount of logs are generated every day, which may overwhelm developers. Logging information overload can be dangerous to software applications. Using log levels, developers can print the useful information while hiding the verbose logs during software runtime. As software evolves, the log levels of logging statements associated with the surrounding software feature implementation may also need to be altered. Maintaining log levels necessitates a significant amount of manual effort. In this paper, we demonstrate an automated approach that can rejuvenate feature log levels by matching the interest level of developers in the surrounding features. The approach is implemented as an open-source Eclipse plugin, using two external plug-ins (JGit and Mylyn). It was tested on 18 open-source Java projects consisting of ~3 million lines of code and ~4K log statements. Our tool successfully analyzes 99.22\% of logging statements, increases log level distributions by ~20\%, and increases the focus of logs in bug fix contexts ~83\% of the time. For further details, interested readers can watch our demonstration video (https://www.youtube.com/watch?v=qIULoAXoDv4).
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Multi-source Log Clustering in Distributed Systems
Distributed systems are seeing wider use as software becomes more complex and cloud systems increase in popularity. Preforming anomaly detection and other log analysis procedures on distributed systems have not seen much research. To this end, we propose a simple and generic method of clustering log statements from separate log files to perform future log analysis. We identify variable components of log statements and find matches of these variables between the sources. After scoring the variables, we select the one with the highest score to be the clustering basis. We performed a case study of our method on the two open-source projects, to which we found success in the results of our method and created an open-source project log-matcher.
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- Award ID(s):
- 1854049
- PAR ID:
- 10310334
- Date Published:
- Journal Name:
- Information Science and Applications. Lecture Notes in Electrical Engineering
- Volume:
- 739
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-981-33-6385-4_4
