Journal Article10.1007/S10664-016-9429-5
Characterizing logging practices in Java-based open source software projects --- a replication study in Apache Software Foundation
Boyuan Chen,Zhen Ming Jiang +1 more
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TL;DR: A replication study of 21 different Java-based open source projects from three different categories shows that all projects contain logging code, which is actively maintained, however, contrary to the original study, bug reports containing log messages take a longer time to resolve than bug reports without log messages.
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Abstract: Log messages, which are generated by the debug statements that developers insert into the code at runtime, contain rich information about the runtime behavior of software systems. Log messages are used widely for system monitoring, problem diagnoses and legal compliances. Yuan et al. performed the first empirical study on the logging practices in open source software systems. They studied the development history of four C/C++ server-side projects and derived ten interesting findings. In this paper, we have performed a replication study in order to assess whether their findings would be applicable to Java projects in Apache Software Foundations. We examined 21 different Java-based open source projects from three different categories: server-side, client-side and supporting-component. Similar to the original study, our results show that all projects contain logging code, which is actively maintained. However, contrary to the original study, bug reports containing log messages take a longer time to resolve than bug reports without log messages. A significantly higher portion of log updates are for enhancing the quality of logs (e.g., formatting & style changes and spelling/grammar fixes) rather than co-changes with feature implementations (e.g., updating variable names).
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Citations
The ABC of Software Engineering Research
Klaas-Jan Stol,Brian Fitzgerald +1 more
TL;DR: A taxonomy from the social sciences is adopted, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies, and six ways in which the framework can advance SE research.
270
•Posted Content
A Survey on Automated Log Analysis for Reliability Engineering.
TL;DR: This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs,How to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis.
Characterizing and detecting anti-patterns in the logging code
Boyuan Chen,Zhen Ming Jiang +1 more
- 20 May 2017
TL;DR: The problem of how-to-log is studied by characterizing and detecting the anti-patterns in the logging code by encoding them into a static code analysis tool, LCAnalyzer, which has an average recall of 95% and precision of 60% and can be used to automatically detect previously unknown anti- patterns in the source code.
130
A Survey on Automated Log Analysis for Reliability Engineering
TL;DR: A detailed overview of automated log analysis research can be found in this paper, where the authors present several promising future directions toward real-world and next-generation automated logging analysis, including how to assist the writing of logging statements, how to compress logs and how to parse logs into structured event templates.
128
Logram: Efficient Log Parsing Using n-Gram Dictionaries
TL;DR: Logram as mentioned in this paper leverages-gram dictionaries to achieve efficient log parsing and 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, achieving 1.8 to 5.1 times faster than the second-fastest approaches in terms of end-to-end parsing time.
111
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