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
129
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
Remote operation of the GOLEM tokamak for Fusion Education
O. Grover,J. Kocman,Michal Odstrcil,T. Odstrcil,M. Matusu,J. Stöckel,V. Svoboda,G. Vondrasek,Jiri Zara +8 more
TL;DR: Tokamak GOLEM is a small, modest device with its infrastructure linked to web technologies allowing students to set-up necessary discharge parameters, submit them into a queue and within minutes obtain the results in the form of a discharge homepage.
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Logging Practices with Mobile Analytics: An Empirical Study on Firebase
Julian Harty,Haonan Zhang,Lili Wei,Luca Pascarella,Maurício Aniche,Weiyi Shang +5 more
- 17 May 2021
TL;DR: In this article, the authors provide an empirical evaluation of the use of Firebase Analytics in 57 open-source Android applications by studying the evolution of code-bases to understand: the needs-in-common that push practitioners to adopt logging practices on mobile devices, and the differences in the ways developers use local and remote logging.
Guiding log revisions by learning from software evolution history
TL;DR: The design and implementation of LogTracker is designed and implemented, an automatic tool that learns log revision rules by mining the correlation between logging context and modifications and recommends candidate log revisions by applying these rules.
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Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection
Boxi Yu,Jiayi Yao,Qiuai Fu,Zhiqi. Zhong,Haotian Xie,Yaoliang Wu,Yuchi Ma,Pinjia He +7 more
- 06 Feb 2024
TL;DR: Deep learning (DL) offers strong performance in log anomaly detection, but comes with high computational costs. DL methods require extensive preprocessing and training time.
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Demystifying the challenges and benefits of analyzing user-reported logs in bug reports
TL;DR: In this article, the authors conduct an empirical study on the challenges that developers may encounter when analyzing the user-provided logs and their benefits and find that many logs may be incomplete or inaccurate, which can cause difficulty for developers to diagnose the bug, and thus, delay the bug fixing process.
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Estimating the reproducibility of psychological science
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TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Data Mining: Concepts and Techniques
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TL;DR: This article explains What is data mining?
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Two case studies of open source software development: Apache and Mozilla
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Detecting large-scale system problems by mining console logs
Wei Xu,Ling Huang,Armando Fox,David A. Patterson,Michael I. Jordan +4 more
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TL;DR: In this article, a general methodology to mine this rich source of information to automatically detect system runtime problems was proposed, combining source code analysis with information retrieval to create composite features and then analyze these features using machine learning to detect operational problems.
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