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Studying the characteristics of logging practices in mobile apps: a case study on F-Droid

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Abstract

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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Notes

  1. https://play.google.com/store/apps/details?id=org.wordpress.android

  2. https://f-droid.org/wiki/page/Repository_Maintenance

  3. http://www.srcml.org/

  4. https://bitbucket.org/sense_concordia/mobilelogreplication

  5. https://github.com/AlDanial/cloc

  6. https://github.com/ahmedre/quran_android/commit/70991626d4cab1542a0e2069d69e752ed2828bea

  7. https://github.com/JakeWharton/timber

  8. https://github.com/ahmedre/quran_android/commit/ff00a294aab44c0b995edced93e1e16d1f3ff086

  9. https://github.com/orhanobut/logger

  10. https://github.com/HashEngineering/dash-wallet/commit/2a96b0d3799a3208e2642905f110a344b5a2d7a3

  11. https://github.com/owncloud/News-Android-App/commit/a768e55207e11d44d27caf27ea9178f3bd37db62

  12. https://github.com/PaperAirplane-Dev-Team/BlackLight/commit/b0ab5187f83c6688facd4acbae803579336e9397

  13. https://github.com/CityZenApp/Android-Development/commit/7a8a6d980e404b5b5a727bca103788670c14db13

  14. https://github.com/Tortel/SysLog/commit/8f5e4b7e6461f1d714c119e2bb33be555258c72c/

  15. https://github.com/thuryn/your-local-weather/commit/6e7be714f500071c0bfc368b0352c92782325ccd

  16. https://github.com/wbaumann/SmartReceiptsLibrary/commit/c0676d4c5ed01e3e86e53ce52a9183a3c33bacb1

  17. https://github.com/mozilla-mobile/focus-android/commit/301818afa33216d9a6421c11908813331a468c9c

  18. https://github.com/ankidroid/Anki-Android/commit/161ef99ab324eb427bc92586e524e42f68886284

  19. https://github.com/whirish/Tri-Valley-Buses/blob/db63698e25b263e9c57f649da555f41814088f52/platforms/android/CordovaLib/src/org/apache/cordova/LOG.java

  20. https://github.com/tejado/Authorizer/blob/68ed954d9965f4ec594a01d06a0be7e26f1fdb82/lib-owncloud/src/main/java/com/owncloud/android/lib/common/utils/Log_OC.java

  21. https://github.com/withad/Mandelbrot-Maps-on-Android/commit/2bf86fc239ea063950ebc8039ba8084b04a6bad1

  22. https://developer.android.com/training/testing/espresso/

  23. https://junit.org/junit5/

  24. https://github.com/giampaolo/psutil

  25. http://man7.org/linux/man-pages/man5/proc.5.html

  26. https://developer.android.com/studio/command-line/dumpsys

  27. https://logging.apache.org/log4j/2.x/

  28. https://github.com/JakeWharton/timber

  29. http://robolectric.org/

  30. https://github.com/orhanobut/logger

  31. https://github.com/JakeWharton/timber

  32. https://github.com/DecentralizedAmateurPagingNetwork/DAPNETApp/commit/bd7427d825a02cde2584858396fa170f7dd0a44d

  33. https://github.com/DecentralizedAmateurPagingNetwork/DAPNETApp/commit/f4a84c261a910b2b9d702028275299f6a4c04663

  34. https://github.com/brarcher/video-transcoder/commit/55c22c594cba15abae60528a55e0309c00c035ca

  35. https://logging.apache.org/log4j/2.x/

  36. https://logging.apache.org/log4j/2.x/

References

  • Android (2017) Log. https://developer.android.com/reference/android/util/Log.html

  • Boulon J, Konwinski A, Qi R, Rabkin A, Yang E, Yang M (2008) Chukwa, a large-scale monitoring system. In: Proceedings of CCA, vol 8, pp 1–5

  • Chen B, Jiang ZMJ (2017) Characterizing logging practices in java-based open source software projects–a replication study in apache software foundation. Empir Softw Eng 22(1):330–374

    Article  Google Scholar 

  • Chen J, Shang W (2017) An exploratory study of performance regression introducing code changes. In: 2017 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 341–352

  • Chen TH, Shang W, Jiang ZM, Hassan AE, Nasser M, Flora P (2014) Detecting performance anti-patterns for applications developed using object-relational mapping. In: Proceedings of the 36th international conference on software engineering. ACM, pp 1001–1012

  • Chen TH, Shang W, Jiang ZM, Hassan AE, Nasser M, Flora P (2016) Finding and evaluating the performance impact of redundant data access for applications that are developed using object-relational mapping frameworks. IEEE Trans Softw Eng 42(12):1148–1161

    Article  Google Scholar 

  • Chowdhury S, Di Nardo S, Hindle A, Jiang ZMJ (2017) An exploratory study on assessing the energy impact of logging on android apps. Empir Softw Eng, 1–35

  • Cliff N (1993) Dominance statistics: ordinal analyses to answer ordinal questions. Psychol Bull 114(3):494

    Article  Google Scholar 

  • Developer A (2017) Write and view logs with logcat. https://developer.android.com/studio/debug/am-logcat#WriteLogs

  • Ding R, Zhou H, Lou JG, Zhang H, Lin Q, Fu Q, Zhang D, Xie T (2015) Log2: a cost-aware logging mechanism for performance diagnosis. In: USENIX annual technical conference, pp 139–150

  • Elye (2018) Debug messages your responsibility to strip it before release!. https://medium.com/@elye.project/debug-messages-your-responsible-to-clear-it-before-release-1a0f872d66f

  • F-Droid (2017) Free and open source android app repository. https://f-droid.org/

  • Fleiss JL, Cohen J (1973) The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ Psychol Measur 33(3):613–619

    Article  Google Scholar 

  • Fu Q, Zhu J, Hu W, Lou JG, Ding R, Lin Q, Zhang D, Xie T (2014) Where do developers log? An empirical study on logging practices in industry. In: Companion proceedings of the 36th international conference on software engineering. ACM, pp 24–33

  • Harpstead E, Zimmermann T, Nagapan N, Guajardo JJ, Cooper R, Solberg T, Greenawalt D (2015) What drives people: creating engagement profiles of players from game log data. In: Proceedings of the 2015 annual symposium on computer-human interaction in play. ACM, pp 369–379

  • Hassani M, Shang W, Shihab E, Tsantalis N (2018) Studying and detecting log-related issues. Empir Softw Eng, 1–33

  • Kabinna S, Bezemer CP, Shang W, Hassan AE (2016a) Logging library migrations: a case study for the apache software foundation projects. In: 2016 IEEE/ACM 13th working conference on mining software repositories (MSR), pp 154–164

  • Kabinna S, Shang W, Bezemer CP, Hassan AE (2016b) Examining the stability of logging statements. In: 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), vol 1. IEEE, pp 326–337

  • Kabinna S, Bezemer CP, Shang W, Syer MD, Hassan AE (2018) Examining the stability of logging statements. Empir Softw Eng 23(1):290–333. https://doi.org/10.1007/s10664-017-9518-0

    Article  Google Scholar 

  • Kernighan BW, Pike R (1999) The practice of programming. Addison-Wesley Longman Publishing Co., Inc., Boston

    Google Scholar 

  • Li H, Shang W, Hassan AE (2017a) Which log level should developers choose for a new logging statement? Empir Softw Eng 22(4):1684–1716

    Article  Google Scholar 

  • Li H, Shang W, Zou Y, Hassan AE (2017b) Towards just-in-time suggestions for log changes. Empir Softw Eng 22(4):1831–1865

    Article  Google Scholar 

  • Li H, Chen THP, Shang W, Hassan AE (2018) Studying software logging using topic models. Empir Softw Eng 23(5):2655–2694. https://doi.org/10.1007/s10664-018-9595-8

    Article  Google Scholar 

  • Lin D, Bezemer CP, Zou Y, Hassan AE (2018) An empirical study of game reviews on the steam platform. Empir Softw Eng, 1–38

  • Malik H, Hemmati H, Hassan AE (2013) Automatic detection of performance deviations in the load testing of large scale systems. In: Proceedings of the 2013 international conference on software engineering, ICSE ’13. IEEE Press, Piscataway, pp 1012–1021. http://dl.acm.org/citation.cfm?id=2486788.2486927

  • Moore DS, Craig BA, McCabe GP (2012) Introduction to the practice of statistics. WH Freeman

  • Murphy-Hill E, Zimmermann T, Bird C, Nagappan N (2015) The design space of bug fixes and how developers navigate it. IEEE Trans Softw Eng 41(1):65–81

    Article  Google Scholar 

  • Mytkowicz T, Diwan A, Hauswirth M, Sweeney PF (2009a) Producing wrong data without doing anything obviously wrong!. ACM Sigplan Not 44(3):265–276

    Article  Google Scholar 

  • Mytkowicz T, Diwan A, Hauswirth M, Sweeney PF (2009b) Producing wrong data without doing anything obviously wrong! SIGPLAN Not 44(3):265–276

    Article  Google Scholar 

  • Nagappan N, Ball T (2005) Use of relative code churn measures to predict system defect density. In: 27th international conference on software engineering, 2005. ICSE 2005. Proceedings. IEEE, pp 284–292

  • Pinjia H, Zhuangbin C, Shilin H, Lyu MR (2018) Characterizing the natural language descriptions in software logging statements. In: Proceedings of the 33rd IEEE/ACM international conference on automated software engineering. IEEE Press

  • Romano J, Kromrey JD, Coraggio J, Skowronek J (2006) Appropriate statistics for ordinal level data: should we really be using t-test and cohen’sd for evaluating group differences on the nsse and other surveys. In: Annual meeting of the Florida association of institutional research, pp 1–33

  • Shang W, Jiang ZM, Adams B, Hassan AE, Godfrey MW, Nasser M, Flora P (2011) An exploratory study of the evolution of communicated information about the execution of large software systems. In: 2011 18th working conference on reverse engineering, pp 335–344

  • Shang W, Jiang ZM, Adams B, Hassan AE, Godfrey MW, Nasser M, Flora P (2014a) An exploratory study of the evolution of communicated information about the execution of large software systems. J Softw Evol Process 26(1):3–26

    Article  Google Scholar 

  • Shang W, Nagappan M, Hassan AE, Jiang ZM (2014b) Understanding log lines using development knowledge. In: 2014 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 21–30

  • Shang W, Nagappan M, Hassan AE (2015) Studying the relationship between logging characteristics and the code quality of platform software. Empir Softw Eng 20(1):1–27. https://doi.org/10.1007/s10664-013-9274-8

    Article  Google Scholar 

  • Shull F, Singer J, Sjøberg DI (2007) Guide to advanced empirical software engineering. Springer

  • StackOverflow (2017) Why doesn’t “system.out.println” work in android? https://stackoverflow.com/a/2220559

  • Syer MD, Jiang ZM, Nagappan M, Hassan AE, Nasser M, Flora P (2013) Leveraging performance counters and execution logs to diagnose memory-related performance issues. In: 2013 IEEE international conference on software maintenance, pp 110–119

  • Tan J, Pan X, Kavulya S, Gandhi R, Narasimhan P (2008) Salsa: analyzing logs as state machines. In: Proceedings of the first USENIX conference on analysis of system logs, WASL’08. USENIX Association, Berkeley, pp 6–6. http://dl.acm.org/citation.cfm?id=1855886.1855892

  • Weiyi S, Ming JZ, Bram A, HA E, GM W, Mohamed N, Parminder F (2013) An exploratory study of the evolution of communicated information about the execution of large software systems. J Softw: Evol Process 26(1):3–26. https://doi.org/10.1002/smr.1579

    Article  Google Scholar 

  • Xu W, Huang L, Fox A, Patterson D, Jordan MI (2009) Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd symposium on operating systems principles, SOSP ’09. ACM, New York, pp 117–132. https://doi.org/10.1145/1629575.1629587

  • Yamane T (1973) Statistics: an introductory analysis

  • Yuan D, Park S, Zhou Y (2012a) Characterizing logging practices in open-source software. In: Proceedings of the 34th international conference on software engineering. IEEE Press, pp 102–112

  • Yuan D, Park S, Huang P, Liu Y, Lee MMJ, Tang X, Zhou Y, Savage S (2012b) Be conservative: enhancing failure diagnosis with proactive logging. In: OSDI, vol 12, pp 293–306

  • Yuan D, Zheng J, Park S, Zhou Y, Savage S (2012c) Improving software diagnosability via log enhancement. ACM Trans Comput Syst (TOCS) 30(1):4

    Article  Google Scholar 

  • Zhao X, Rodrigues K, Luo Y, Stumm M, Yuan D, Zhou Y (2017) Log20: fully automated optimal placement of log printing statements under specified overhead threshold. In: Proceedings of the 26th symposium on operating systems principles. ACM, pp 565–581

  • Zhu J, He P, Fu Q, Zhang H, Lyu MR, Zhang D (2015) Learning to log: helping developers make informed logging decisions. In: Proceedings of the 37th international conference on software engineering, vol 1. IEEE Press, pp 415–425

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Acknowledgements

The authors gratefully thank the developers who participated and shared their thoughts in our email interview.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada

    Yi Zeng, Jinfu Chen, Weiyi Shang & Tse-Hsun (Peter) Chen

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  1. Yi Zeng
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  2. Jinfu Chen
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  4. Tse-Hsun (Peter) Chen
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Correspondence to Yi Zeng.

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Communicated by: David Lo, Meiyappan Nagappan, Fabio Palomba, and Sebastiano Panichella

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Zeng, Y., Chen, J., Shang, W. et al. Studying the characteristics of logging practices in mobile apps: a case study on F-Droid. Empir Software Eng 24, 3394–3434 (2019). https://doi.org/10.1007/s10664-019-09687-9

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