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ICSE 2021
Mon 17 May - Sat 5 June 2021

Software systems usually record important runtime information in their logs. Logs help practitioners understand system runtime behaviors and diagnose field failures. As logs are usually very large in size, automated log analysis is needed to assist practitioners in their software operation and maintenance efforts. Typically, the first step of automated log analysis is log parsing, i.e., converting unstructured raw logs into structured data. However, log parsing is challenging, because logs are produced by static templates in the source code (i.e., logging statements) yet the templates are usually inaccessible when parsing logs. Prior work proposed automated log parsing approaches that have achieved high accuracy. However, as the volume of logs grows rapidly in the era of cloud computing, efficiency becomes a major concern in log parsing. In this work, we propose an automated log parsing approach, Logram, which leverages n-gram dictionaries to achieve efficient log parsing. We evaluated Logram on 16 public log datasets and compared Logram with five state-of-the-art log parsing approaches. We found that Logram achieves a higher parsing accuracy than the best existing approaches (i.e., at least 10% higher, on average) and also outperforms these approaches in efficiency (i.e., 1.8 to 5.1 times faster than the second-fastest approaches in terms of end-to-end parsing time). Furthermore, we deployed Logram on Spark and we found that Logram scales out efficiently with the number of Spark nodes (e.g., with near-linear scalability for some logs) without sacrificing parsing accuracy. In addition, we demonstrated that Logram can support effective online parsing of logs, achieving similar parsing results and efficiency to the offline mode.

Conference Day
Tue 25 May

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

19:35 - 20:55
1.5.1. Deep Neural Networks: General IssuesTechnical Track / Journal-First Papers / SEIP - Software Engineering in Practice at Blended Sessions Room 1 +12h
Chair(s): Ignacio PanachUniversidad de Valencia
19:35
20m
Paper
Asset Management in Machine Learning: A SurveySEIP
SEIP - Software Engineering in Practice
Samuel IdowuChalmers | University of Gothenburg, Daniel StrüberRadboud University Nijmegen, Thorsten BergerChalmers | University of Gothenburg
Pre-print Media Attached
19:55
20m
Paper
An Empirical Study of Refactorings and Technical Debt in Machine Learning SystemsTechnical Track
Technical Track
Yiming TangCity University of New York (CUNY) Graduate Center, Raffi KhatchadourianCUNY Hunter College, Mehdi BagherzadehOakland University, Rhia SinghCity University of New York (CUNY) Macaulay Honors College, Ajani StewartCity University of New York (CUNY) Hunter College, Anita RajaCity University of New York (CUNY) Hunter College
Pre-print Media Attached
20:15
20m
Paper
Logram: Efficient Log Parsing Using n-Gram DictionariesJournal-First
Journal-First Papers
Hetong DaiConcordia University, Heng LiPolytechnique Montréal, Che-Shao ChenConcordia University, Weiyi ShangConcordia University, Tse-Hsun (Peter) ChenConcordia University
DOI Pre-print Media Attached
20:35
20m
Paper
DeepLocalize: Fault Localization for Deep Neural NetworksTechnical Track
Technical Track
Mohammad WardatDept. of Computer Science, Iowa State University, Wei LeDept. of Computer Science, Iowa State University, Hridesh RajanDept. of Computer Science, Iowa State University
Pre-print Media Attached

Conference Day
Wed 26 May

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

07:35 - 08:55
07:35
20m
Paper
Asset Management in Machine Learning: A SurveySEIP
SEIP - Software Engineering in Practice
Samuel IdowuChalmers | University of Gothenburg, Daniel StrüberRadboud University Nijmegen, Thorsten BergerChalmers | University of Gothenburg
Pre-print Media Attached
07:55
20m
Paper
An Empirical Study of Refactorings and Technical Debt in Machine Learning SystemsTechnical Track
Technical Track
Yiming TangCity University of New York (CUNY) Graduate Center, Raffi KhatchadourianCUNY Hunter College, Mehdi BagherzadehOakland University, Rhia SinghCity University of New York (CUNY) Macaulay Honors College, Ajani StewartCity University of New York (CUNY) Hunter College, Anita RajaCity University of New York (CUNY) Hunter College
Pre-print Media Attached
08:15
20m
Paper
Logram: Efficient Log Parsing Using n-Gram DictionariesJournal-First
Journal-First Papers
Hetong DaiConcordia University, Heng LiPolytechnique Montréal, Che-Shao ChenConcordia University, Weiyi ShangConcordia University, Tse-Hsun (Peter) ChenConcordia University
DOI Pre-print Media Attached
08:35
20m
Paper
DeepLocalize: Fault Localization for Deep Neural NetworksTechnical Track
Technical Track
Mohammad WardatDept. of Computer Science, Iowa State University, Wei LeDept. of Computer Science, Iowa State University, Hridesh RajanDept. of Computer Science, Iowa State University
Pre-print Media Attached