A Three Dimensional Empirical Study of Logging Questions From Six Popular Q&A Websites

Gujral, Harshit; Sharma, Abhinav; Lal, Sangeeta; Kumar, Lov

Abstract

Background: Q&A websites such as StackOverflow, Serverfault, provide an open platform for users to ask questions and to get help from experts present worldwide. These websites not only help users by answering their questions but also act as a knowledge base. These data present on these websites can be mined to extract valuable information that can benefit the software practitioners. Software engineering research community has already understood the potential benefits of mining data from Q&A websites and several research studies have already been conducted in this area. Aim: The aim of the study presented in this paper is to perform an empirical analysis of logging questions from six popular Q&A websites. Method: We perform statistical, programming language and content analysis of logging questions. Our analysis helped us to gain insight about the logging discussion happening in six different domains of the StackExchange websites. Results: Our analysis provides insight about the logging issues of software practitioners: logging questions are pervasive in all the Q&A websites, the mean time to get accepted answer for logging questions on SU and SF websites are much higher as compared to other websites, a large number of logging question invite a great amount of discussion in the SoftwareEngineering Q&A website, most of the logging issues occur in C++ and Java, the trend for number of logging questions is increasing for Java, Python, and Javascript, whereas, it is decreasing or constant for C, C++, C#, for the ServerFault and Superuser website 'C' is the dominant programming language.

Citation

Gujral, H., Sharma, A., Lal, S., & Kumar, L. (2019). A Three Dimensional Empirical Study of Logging Questions From Six Popular Q&A Websites. e-Informatica Software Engineering Journal (EISEJ), 13(1), 105--139. https://doi.org/10.5277/e-Inf190104

• Journal Article Type: Article
• Acceptance Date: Feb 28, 2019
• Online Publication Date: May 31, 2019
• Publication Date: 2019
• Deposit Date: Jul 25, 2024
• Journal: e-Informatica Software Engineering Journal
• Print ISSN: 1897-7979
• Electronic ISSN: 2084-4840
• Publisher: Software Engineering Section of the Committee on Informatics of the Polish Academy of Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Pages: 105--139
• DOI: https://doi.org/10.5277/e-Inf190104
• Keywords: classification, debugging, ensemble, logging, machine learning, source code analysis, tracing
• Public URL: https://keele-repository.worktribe.com/output/879860
• Publisher URL: http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_13/eInformatica2019Art04.pdf