Studying the Characteristics of Logging Practices in Mobile Apps: A Case Study on F-Droid.
Logging is a common practice in software engineering. Prior research has investigated the characteristics of logging practices in system software (e.g., web servers or databases) as well as desktop applications. However, despite the popularity of mobile apps, little is known about their logging practices. In this paper, we sought to study logging practices in mobile apps. In particular, we conduct a case study on 1,444 open source Android apps in the F-Droid repository. Through a quantitative study, we find that although mobile app logging is less pervasive than server and desktop applications, logging is leveraged in almost all studied apps. However, we find that there exist considerable differences between the logging practices of mobile apps and the logging practices in server and desktop applications observed by prior studies. In order to further understand such differences, we conduct a firehouse email interview and a qualitative annotation on the rationale of using logs in mobile app development. By comparing the logging level of each logging statement with developers’ rationale of using the logs, we find that all too often (35.4%), the chosen logging level and the rationale are inconsistent. Such inconsistency may prevent the useful runtime information to be recorded or may generate unnecessary logs that may cause performance overhead. Finally, to understand the magnitude of such performance overhead, we conduct a performance evaluation between generating all the logs and not generating any logs in eight mobile apps. In general, we observe a statistically significant performance overhead based on various performance metrics (response time, CPU and battery consumption). In addition, we find that if the performance overhead of logging is significantly observed in an app, disabling the unnecessary logs indeed provides a statistically significant performance improvement. Our results show the need for a systematic guidance and automated tool support to assist in mobile logging practices.
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Brittany JohnsonUniversity of Massachusetts Amherst, Yuriy BrunUniversity of Massachusetts Amherst, Alexandra MeliouUniversity of Massachusetts AmherstLink to publication DOI Pre-print Media Attached
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Yi ZengConcordia University, Jinfu ChenConcordia University, Canada, Weiyi ShangConcordia University, Tse-Hsun (Peter) ChenConcordia UniversityAuthorizer link Pre-print
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Nathan ChongAmazon, Byron CookAmazon, Konstantinos KallasUniversity of Pennsylvania, Kareem KhazemAmazon, Felipe R. MonteiroAmazon, Daniel Schwartz-NarbonneAmazon, n.n., Serdar TasiranAmazon, n.n., Michael TautschnigAmazon Web Services, Mark R. TuttleAmazonPre-print Media Attached