Logging Practices with Mobile Analytics: An Empirical Study on Firebase

Julian Harty, Haonan Zhang, Lili Wei, Luca Pascarella, Maurício Aniche, Weiyi Shang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs from the end users at the cost of recording and transmitting logs across the Internet to a centralised infrastructure. The goal of this paper is to make the first step in the characterisation of logging practices of a widely adopted mobile analytics logging library, namely Firebase Analytics. With this study, we aim to understand what are the common developers' needs that push practitioners to adopt logging practices on mobile devices.

We propose an empirical evaluation of the use of Firebase Analytics in open-source Android applications that shows how mobile analytics logs are less pervasive and less maintained than traditional logging code. Finally, while the main goal of traditional logging consists in gathering information for debugging purposes, logging becomes more user centered when mobile analytics is used for logging.
Original languageEnglish
Title of host publication2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)
EditorsL. O'Conner
Place of PublicationPiscataway
PublisherIEEE
Number of pages5
ISBN (Electronic)978-1-7281-8711-2
ISBN (Print)978-1-6654-2986-3
DOIs
Publication statusPublished - 2021
Event2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft) - Virtual at Madrid, Spain
Duration: 17 May 202119 May 2021
Conference number: 8th

Conference

Conference2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft)
CountrySpain
CityVirtual at Madrid
Period17/05/2119/05/21

Keywords

  • mobile analytics
  • mobile software development
  • logging engineering
  • software monitoring
  • empirical software engineering

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