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ICSE 2021
Mon 17 May - Sat 5 June 2021

Software log analysis helps to maintain the health of software solutions and ensure compliance and security. Existing software systems consist of heterogeneous components emitting logs in various formats. A typical solution is to unify the logs using manually built parsers, which is laborious.

Instead, we explore the possibility of automating the parsing task by employing machine translation (MT). We create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models. Models’ evaluation on real-world logs shows that the models can learn Apache log format and parse individual log records. The median relative edit distance between an actual real-world log record and the MT prediction is less than or equal to 28%. Thus, we show that log parsing using an MT approach is promising.

Wed 26 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:30 - 15:25
14:30
20m
Paper
Semi-supervised Log-based Anomaly Detection via Probabilistic Label EstimationArtifact ReusableTechnical TrackArtifact Available
Technical Track
Lin Yang College of Intelligence and Computing, Tianjin University, Junjie Chen College of Intelligence and Computing, Tianjin University, Zan Wang College of Intelligence and Computing, Tianjin University, Weijing Wang College of Intelligence and Computing, Tianjin University, Jiajun Jiang College of Intelligence and Computing, Tianjin University, Xuyuan Dong Information and Network Center,Tianjin University, Wenbin Zhang Information and Network Center,Tianjin University
Pre-print Media Attached
14:50
20m
Paper
DeepLV: Suggesting Log Levels Using Ordinal Based Neural NetworksTechnical Track
Technical Track
Zhenhao Li Concordia University, Heng Li Polytechnique Montréal, Tse-Hsun (Peter) Chen Concordia University, Weiyi Shang Concordia University
Pre-print Media Attached
15:10
15m
Short-paper
On Automatic Parsing of Log RecordsNIER
NIER - New Ideas and Emerging Results
Jared Rand Ryerson University, Andriy Miranskyy Ryerson University
Pre-print Media Attached

Thu 27 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

02:30 - 03:25
02:30
20m
Paper
Semi-supervised Log-based Anomaly Detection via Probabilistic Label EstimationArtifact ReusableTechnical TrackArtifact Available
Technical Track
Lin Yang College of Intelligence and Computing, Tianjin University, Junjie Chen College of Intelligence and Computing, Tianjin University, Zan Wang College of Intelligence and Computing, Tianjin University, Weijing Wang College of Intelligence and Computing, Tianjin University, Jiajun Jiang College of Intelligence and Computing, Tianjin University, Xuyuan Dong Information and Network Center,Tianjin University, Wenbin Zhang Information and Network Center,Tianjin University
Pre-print Media Attached
02:50
20m
Paper
DeepLV: Suggesting Log Levels Using Ordinal Based Neural NetworksTechnical Track
Technical Track
Zhenhao Li Concordia University, Heng Li Polytechnique Montréal, Tse-Hsun (Peter) Chen Concordia University, Weiyi Shang Concordia University
Pre-print Media Attached
03:10
15m
Short-paper
On Automatic Parsing of Log RecordsNIER
NIER - New Ideas and Emerging Results
Jared Rand Ryerson University, Andriy Miranskyy Ryerson University
Pre-print Media Attached