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Studying and Detecting Log-related Issues

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Studying and Detecting Log-related Issues

Mehran, Hassani (2018) Studying and Detecting Log-related Issues. Masters thesis, Concordia University.

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Abstract

Logs capture valuable information throughout the execution of software systems. The rich knowledge conveyed in logs is leveraged by researchers and practitioners in performing various tasks, both in software development and its operation. Log-related issues, such as missing or having outdated information, may have a large impact on the users who depend on these logs. In this paper, we first perform an empirical study on log-related issues in two large-scale, open-source software systems. We found that the files with log-related issues have undergone statistically significantly more frequent prior changes, and bug fixes. We also found that developers fixing these log-related issues are often not the ones who introduced the logging statement nor the owner of the method containing the logging statement. Maintaining logs is more challenging without experts. Finally, we found that most of the defective logging statements remain unreported for a long period (median 320 days). Once reported, the issues are fixed quickly (median five days). Our empirical findings suggest the need for automated tools that can detect log-related issues promptly. We conducted a manual study and identified seven root-causes of the log-related issues. Based on these root causes, we developed an automated tool that detects four types of log-related issues. Our tool can detect 78 existing inappropriate logging statements reported in 40 log-related issues. We also reported new issues found by our tool to developers and 38 previously unknown issues in the latest release of the subject systems were accepted by developers.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Mehran, Hassani
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:16 March 2018
Thesis Supervisor(s):Shang, Weiyi and Tsantalis, Nikolaos
ID Code:983569
Deposited By: Mehran Hassani
Deposited On:11 Jun 2018 03:37
Last Modified:11 Jun 2018 03:37
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