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ICPC 2022
Mon 16 - Tue 17 May 2022
co-located with ICSE 2022
Mon 16 May 2022 11:31 - 11:38 at ICPC room - Session 7: Debugging 1 Chair(s): Kevin Moran

In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messages: the developer provides hints to the (unknown) operator, where the cause of an occurred issue is, and vice versa, the operator can report bugs during operation. To fulfil this purpose, developers write log instructions that are structured text commonly composed of a log level (e.g., “info", “error”), static text (“IP {} cannot be reached”), and dynamic variables (e.g. IP {}). However, opposed to well-adopted coding practices, there are no widely adopted guidelines on how to write log instructions with good quality properties. For example, a developer may assign a high log level (e.g., “error”) for a trivial event that can confuse the operator and increase maintenance costs. Or the static text can be insufficient to hint at a specific issue. In this paper, we address the problem of log quality assessment and provide the first step towards its automation. We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description. Based on these findings, we developed a data-driven approach that adapts deep learning methods for each of the two properties. An extensive evaluation of nine large-scale open-source systems shows that our approach correctly assesses log level assignments with an accuracy of 0.88, and the sufficient linguistic structure with an F1 score of 0.99, outperforming the baselines. Our study highlights the potential of the data-driven methods in assessing log instructions quality and aid developers in comprehending and writing better code.

Mon 16 May

Displayed time zone: Eastern Time (US & Canada) change

11:10 - 12:10
Session 7: Debugging 1Research at ICPC room
Chair(s): Kevin Moran George Mason University
11:10
7m
Talk
Causette: User-Controlled Rearrangement of Causal Constructs in a Code Editor
Research
Alice Martin ENAC - Université de Toulouse, Mathieu Magnaudet ENAC - Université de Toulouse, Stéphane Conversy ENAC - Université de Toulouse
File Attached
11:17
7m
Talk
Error Identification Strategies for Python Jupyter Notebooks
Research
Derek Robinson University of Victoria, Neil Ernst University of Victoria, Enrique Larios Vargas University of Victoria, Margaret-Anne Storey University of Victoria
Media Attached
11:24
7m
Talk
Performance Anomaly Detection through Sequence Alignment of System-Level Traces
Research
Madeline Janecek Brock University, Naser Ezzati Jivan , Wahab Hamou-Lhadj Concordia University, Montreal, Canada
Media Attached
11:31
7m
Talk
QuLog: Data-Driven Approach for Log Instruction Quality Assessment
Research
Jasmin Bogatinovski Technical University Berlin, Sasho  Nedelkoski  TU Berlin, Alexander Acker Technical University Berlin, Jorge Cardoso Huawei Munich Research Center, Odej  Kao Technische UniversitÀt Berlin
Media Attached
11:38
7m
Talk
Fixing Continuous Integration Tests From Within the IDE With Contextual Information
Research
Casper Boone Delft University of Technology, Carolin Brandt Delft University of Technology, Andy Zaidman Delft University of Technology
DOI Pre-print Media Attached
11:45
7m
Talk
Shape-Analysis Driven Memory Graph Visualization
Research
Jan H. Boockmann University of Bamberg, Gerald LĂŒttgen University of Bamberg
Media Attached
11:52
18m
Live Q&A
Q&A-Paper Session 7
Research


Information for Participants
Mon 16 May 2022 11:10 - 12:10 at ICPC room - Session 7: Debugging 1 Chair(s): Kevin Moran
Info for room ICPC room:

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