Logs capture valuable information throughout the execution of software systems. The rich knowledge conveyed in logs is highly 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 Log-related issues, such as missing or having outdated information, may have a large impact on the users who depend on these logs.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 find that the files with log-related issues have undergone statistically significantly more frequent prior changes, and bug fixes. We also find 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 clear experts. Finally, we find 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 evident types of log-related issues. Our tool can detect 75 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.
Tue 6 Nov Times are displayed in time zone: Guadalajara, Mexico City, Monterrey change
10:30 - 12:00: Log MiningResearch Papers / Journal-First at Horizons 6-9F Chair(s): Dongyoon LeeVirginia Tech, USA | |||
10:30 - 10:52 Talk | Studying and Detecting Log-Related Issues Journal-First Mehran Hassani, Weiyi ShangConcordia University, Canada, Emad ShihabConcordia University, Nikolaos TsantalisConcordia University, Canada DOI | ||
10:52 - 11:15 Talk | VT-Revolution: Interactive Programming Video Tutorial Authoring and Watching System Journal-First Lingfeng Bao Zhejiang University City College, Zhenchang XingAustralia National University, Xin XiaMonash University, David LoSingapore Management University DOI | ||
11:15 - 11:37 Talk | Using Finite-State Models for Log Differencing Research Papers Hen AmarTel Aviv University, Israel, Lingfeng Bao Zhejiang University City College, Nimrod BusanyTel Aviv University, Israel, David LoSingapore Management University, Shahar MaozTel Aviv University | ||
11:37 - 12:00 Talk | Identifying Impactful Service System Problems via Log Analysis Research Papers Shilin HeChinese University of Hong Kong, Qingwei LinMicrosoft, China, Jian-Guang LouMicrosoft Research, Hongyu ZhangThe University of Newcastle, Michael Lyu, Dongmei ZhangMicrosoft Research, China |