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Automatic and Scalable Fault Detection for Mobile Applications

Automatic and Scalable Fault Detection for Mobile Applications. MobiSys ’ 14 Presented by Haocheng Huang huanghc@emnets.org. Goal. B uild a scalable, easy to use system that tests mobile apps for common, externally-inducible faults as thoroughly as possible .

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Automatic and Scalable Fault Detection for Mobile Applications

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  1. Automatic and Scalable Fault Detection for Mobile Applications MobiSys’ 14 Presented by Haocheng Huang huanghc@emnets.org

  2. Goal • Build a scalable, easy to use system that tests mobile apps for common, externally-inducible faults as thoroughly as possible. • Does not detect all app failures

  3. Analyse Root Cause of Crashes • Partition crash reports that we believe originate due to the same root cause into a collection called a crash bucket. • Use two techniques to determine the likely root cause of its crashes • Use data mining techniques • Searchvarious Windows Phone developer forums

  4. VarnaSena Architecture

  5. Monkey design

  6. UI Automator • Two decisions • what UI control to interact with next • how long to wait before picking the next control • Problems in usual design • multiple controls often lead to the same next page • some controls do not have any event handlers attached to them • a systematic exploration can lead to dead ends • Address the first two issues using hit testing, the third by running multiple independent random monkeys concurrently

  7. UI AutomatorHit Testing • The instrumentation framework instruments all UI event handlers in an app with a hit test monitor. • It also assigns each event handler a unique ID.

  8. UI AutomatorWhen to interact next? • Add a transaction tracker that monitors the transaction at runtime and generates a ProcessingCompletedevent when all the synchronous and asynchronous processing associated with an interaction is complete

  9. UI Automatorwork flow

  10. Inducing Faults • Built as an extensible module in which various fault inducing modules (FIM) can be plugged in.

  11. Evaluation • VanarSena flagged 2969 unique crashes3 in 1108 apps.

  12. Evaluation • Found 1,227 crashes not in the WPER database

  13. Evaluation

  14. Conclusion

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