AutoLog: Facing log redundancy and insufficiency

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

doi:10.1145/2103799.2103811

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 proceeding › Conference contribution

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11
DOIs	https://doi.org/10.1145/2103799.2103811
Publication status	Published - 2011
Externally published	Yes
Event	2nd Asia-Pacific Workshop on Systems, APSys'11 - Shanghai, China Duration: Jul 11 2011 → Jul 12 2011

Publication series

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

Other

Other	2nd Asia-Pacific Workshop on Systems, APSys'11
Country/Territory	China
City	Shanghai
Period	7/11/11 → 7/12/11

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

Access to Document

10.1145/2103799.2103811

Cite this

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title = "AutoLog: Facing log redundancy and insufficiency",

abstract = "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.",

author = "Cheng Zhang and Zhenyu Guo and Ming Wu and Longwen Lu and Yu Fan and Jianjun Zhao and Zheng Zhang",

year = "2011",

doi = "10.1145/2103799.2103811",

language = "English",

isbn = "9781450311793",

series = "Proceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11",

booktitle = "Proceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11",

}

TY - GEN

T1 - AutoLog

T2 - 2nd Asia-Pacific Workshop on Systems, APSys'11

AU - Zhang, Cheng

AU - Guo, Zhenyu

AU - Wu, Ming

AU - Lu, Longwen

AU - Fan, Yu

AU - Zhao, Jianjun

AU - Zhang, Zheng

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

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U2 - 10.1145/2103799.2103811

DO - 10.1145/2103799.2103811

M3 - Conference contribution

AN - SCOPUS:84863126466

SN - 9781450311793

T3 - Proceedings of the 2nd Asia-Pacific Workshop on Systems, APSys'11

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

Y2 - 11 July 2011 through 12 July 2011

ER -

AutoLog: Facing log redundancy and insufficiency

Abstract

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