AutoLog: Facing log redundancy and insufficiency

Cheng Zhang, Zhenyu Guo, Ming Wu, Longwen Lu, Yu Fan, Jianjun Zhao, Zheng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Logs are valuable for failure diagnosis and software debugging in practice. However, due to the ad-hoc style of inserting logging statements, the quality of logs can hardly be guaranteed. In case of a system failure, the log file may contain a large number of irrelevant logs, while crucial clues to the root cause may still be missing. In this paper, we present an automated approach to log improvement based on the combination of information from program source code and textual logs. It selects the most relevant ones from an ocean of logs to help developers focus and reason along the causality chain, and generates additional informative logs to help developers discover the root causes of failures. We have conducted a preliminary case study using an implementation prototype to demonstrate the usefulness of our approach.

Original languageEnglish
Title of host publicationProceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11
Publication statusPublished - 2011
Externally publishedYes
Event2nd Asia-Pacific Workshop on Systems, APSys'11 - Shanghai, China
Duration: Jul 11 2011Jul 12 2011

Publication series

NameProceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11


Other2nd Asia-Pacific Workshop on Systems, APSys'11

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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