Logging Practices with Mobile Analytics: An Empirical Study on Firebase

Abstract

Software logs are of great value in both industrial and open-source projects. Mobile analytics logging enables developers to collect logs remotely from their apps running on end user devices at the cost of recording and transmitting logs across the Internet to a centralised infrastructure.This paper makes a first step in characterising logging practices with a widely adopted mobile analytics logging library, namely Firebase Analytics. We 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: a) the needs-in-common that push practitioners to adopt logging practices on mobile devices, and b) the differences in the ways developers use local and remote logging.Our results indicate mobile analytics logs are less pervasive and less maintained than traditional logging code. Based on our analysis, we believe logging using mobile analytics is more user centered compared to traditional logging, where the latter is mainly used to record information for debugging purposes.

Research on Third-Party Libraries in AndroidApps: A Taxonomy and Systematic LiteratureReview

“…Nevertheless, other TPLs still have some unique features; we should understand them in-depth. We only collected papers from 2012 to 2020, but we have found a few papers have begun to focus on other type of TPLs, such as the analytic libraries [108,141]. Harty et al [141] conducted an empirical study on Google Firebase that is an analytic TPL.…”

“…We only collected papers from 2012 to 2020, but we have found a few papers have begun to focus on other type of TPLs, such as the analytic libraries [108,141]. Harty et al [141] conducted an empirical study on Google Firebase that is an analytic TPL. They found its logs are less pervasive and less maintained than traditional logging code.…”

Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps

“…However, TPLs may collect user data that is not necessary for providing related services. Such behaviors may lead to privacy leakage or even security concerns [5]- [7]. Besides, the permission mechanism of Android also increases the possibility of leaking user privacy by TPLs [8]- [18].…”

Using Deep Learning to Generate Complete Log Statements

“…Inspecting log messages is a popular practice that helps developers in several software maintenance activities such as testing [11,12], debugging [39], diagnosis [47,52], and monitoring [18,19]. Developers insert log statements to expose and register information about the internal behavior of a software artifact in a humancomprehensible fashion [36].…”