An exploratory semantic analysis of logging questions

Gujral, Harshit; Lal, Sangeeta; Li, Heng

Abstract

Logging is an integral part of software development. Software practitioners often face issues in software logging, and they post these issues on Q&A websites to take suggestions from the experts. In this study, we perform a three-level empirical analysis of logging questions posted on six popular technical Q&A websites, namely, Stack Overflow (SO), Serverfault (SF), Superuser (SU), Database Administrators (DB), Software Engineering (SE), and Android Enthusiasts (AE). The findings show that logging issues are prevalent across various domains, for example, database, networks, and mobile computing, and software practitioners from different domains face different logging issues. The semantic analysis of logging questions using Latent Dirichlet Allocation (LDA) reveals trends of several existing and new logging topics, such as logging conversion pattern, Android device logging, and database logging. In addition, we observe specific logging topics for each website: DB (log shipping and log file growing/shrinking), SU (event log and syslog configuration), SF (log analysis and syslog configuration), AE (app install and usage tracking), SE (client server logging and exception logging), and SO (log file creation/deletion, Android emulator logging, and logger class of Log4j). We obtain an increasing trend of logging topics on the SO, SU, and DB websites whereas a decreasing trend of logging topics on the SF website.

Citation

Gujral, H., Lal, S., & Li, H. (2022). An exploratory semantic analysis of logging questions. Journal of Software: Evolution and Process, 33(7), Article ARTN e2361. https://doi.org/10.1002/smr.2361

• Journal Article Type: Article
• Acceptance Date: May 2, 2021
• Online Publication Date: Jun 16, 2021
• Publication Date: 2022-07
• Deposit Date: Jun 5, 2023
• Journal: Journal of Software: Evolution and Process
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 7
• Article Number: ARTN e2361
• DOI: https://doi.org/10.1002/smr.2361
• Keywords: Software