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iPerfDetector: Characterizing and detecting performance anti-patterns in iOS applications

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Abstract

Performance issues in mobile applications (i.e., apps) often have a direct impact on the user experience. However, due to limited testing resources and fast-paced software development cycles, many performance issues remain undiscovered when the apps are released. As found by a prior study, these performance issues are one of the most common complaints that app users have. Unfortunately, there is a limited support to help developers avoid or detect performance issues in mobile apps. In this paper, we conduct an empirical study on performance issues in iOS apps written in Swift language. To the best of our knowledge, this is the first study on performance issues of apps on the iOS platform. We manually studied 225 performance issues that are collected from four open source iOS apps. We found that most performance issues in iOS apps are related to inefficient UI design, memory issues, and inefficient thread handling. We also manually uncovered four performance anti-patterns that recurred in the studied issue reports. To help developers avoid these performance anti-patterns in the code, we implemented a static analysis tool called iPerfDetector. We evaluated iPerfDetector on eight open source and three commercial apps. iPerfDetector successfully detected 34 performance anti-pattern instances in the studied apps, where 31 of them are already confirmed and accepted by developers as potential performance issues. Our case study on the performance impact of the anti-patterns shows that fixing the anti-pattern may improve the performance (i.e., response time, GPU, or CPU) of the workload by up to 80%.

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Notes

  1. https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/

  2. https://github.com/mozilla-mobile/firefox-ios

  3. https://github.com/wordpress-mobile/WordPress-iOS

  4. https://github.com/wireapp/wire-ios

  5. https://github.com/danielgindi/Charts

  6. https://github.com/wordpress-mobile/WordPress-iOS/pull/578

  7. https://github.com/wordpress-mobile/WordPress-iOS/pull/578

  8. https://github.com/wordpress-mobile/WordPress-iOS/pull/578

  9. https://github.com/mozilla-mobile/firefox-ios/pull/1215/commits/4ba952a58bf34ecfa555a8058f7c9a20c154997d

  10. https://stackoverflow.com/questions/28137380/updating-ui-from-background-thread-swift

  11. https://github.com/wordpress-mobile/WordPress-iOS/pull/7864

  12. https://github.com/wordpress-mobile/WordPress-iOS/pull/7864

  13. https://bugzilla.mozilla.org/show_bug.cgi?id=1191058

  14. https://bugzilla.mozilla.org/show_bug.cgi?id=1191058

  15. https://bugzilla.mozilla.org/show_bug.cgi?id=1191058

  16. https://github.com/wordpress-mobile/WordPress-iOS/issues/419

  17. https://github.com/mozilla-mobile/firefox-ios/pull/1215/commits/4ba952a58bf34ecfa555a8058f7c9a20c154997d

  18. https://stackoverflow.com/questions/9270723/what-is-the-better-way-to-set-an-uiview-backgroundcolor-to-transparent

  19. https://www.linkedin.com/company/seeb/

  20. https://github.com/3lvis/Sync/issues/509

  21. https://github.com/hilen/TSWeChat/issues/42

  22. https://github.com/mozilla-mobile/firefox-ios/issues/3961

  23. https://github.com/wordpress-mobile/WordPress-iOS/pull/578

  24. https://github.com/wordpress-mobile/WordPress-iOS/issues/9993

  25. https://github.com/wordpress-mobile/WordPress-iOS/pull/10111

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Authors and Affiliations

  1. Department of Software Engineering and Computer Science, Software PErformance, Analysis, and Reliability (SPEAR) Lab, Concordia University, Montreal, Canada

    Sara Seif Afjehei & Tse-Hsun (Peter) Chen

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

    Nikolaos Tsantalis

Authors
  1. Sara Seif Afjehei
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  2. Tse-Hsun (Peter) Chen
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  3. Nikolaos Tsantalis
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Corresponding author

Correspondence to Tse-Hsun (Peter) Chen.

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

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Afjehei, S.S., Chen, TH.(. & Tsantalis, N. iPerfDetector: Characterizing and detecting performance anti-patterns in iOS applications. Empir Software Eng 24, 3484–3513 (2019). https://doi.org/10.1007/s10664-019-09703-y

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