Best Dissertation Symposium: Reducing user-perceived latency in mobile applications via prefetching and caching

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Reducing User-Perceived Latency in Mobile Applications via Prefetching and Caching Yixue Zhao Advisor: Nenad Medvidović Computing Innovation Fellow (CIFellow) University of Massachusetts Amherst

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Latency!

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Latency Problem Angry Users Sales Loss 5

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Main Cause: Network Transfer Network round trip Mobile App Remote Server Main cause of user-perceived latency is network transfer since mobile apps spend most time fetching data from remote servers. [Ravindranath et al. OSDI’12] 6

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Effective Solution! Problem: Annoying loading wheel L Solution: Prefetching and Caching 7

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Prefetching and Caching local cache on the phone immediate response! 8

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Prefetching and Caching 9 ? But how?

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10 Literature Study Techniques Testing

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1. Two categories Literature Study Techniques Testing 11

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Two Categories q Content-based q No existing solution q Call for novel idea q History-based q Applicable existing solution q Adapt to mobile domain 12

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q Content-based q No existing solution q Call for novel idea q History-based q Applicable existing solution q Adapt to mobile domain 13 Techniques Two Categories

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14 2. Is it a good idea? Literature Study Techniques Testing

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15 Literature Study Techniques Testing 2. Is it a good idea?

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16 3a. Content-based (first work) Literature Study Techniques Testing

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Content-based: PALOMA Key Insights q App’s code has all we need q “User think time” provides opportunities 17

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PALOMA at a High Level Code Analysis App Rewriting Optimized App 18

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PALOMA at a High Level 19 Automatic No other information required 99% latency reduction! Code Analysis App Rewriting Optimized App

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20 Literature Study Techniques Testing 3b. History-based (practical solution)

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History-based: HiPHarness 21 Existing Solutions HiPHarness q Large prediction models q Big cost q Privacy issues

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History-based: HiPHarness 22 Existing Solutions HiPHarness q Large prediction models q Big cost q Privacy issues q Small prediction models! q Good training data q Enough training data

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History-based: HiPHarness 23 Existing Solutions HiPHarness q Large prediction models q Big cost q Privacy issues q Small prediction models! q Good training data q Enough training data 94% smaller!

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HiPHarness Dataset q 10,000+ mobile users q 15+ million network requests q 7+ million prediction models 24

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q Small prediction models work great! q Win-win (reduce size AND improve accuracy) q Challenged prior conclusion q Re-opened this area HiPHarness Takeaways 25 Less is More!

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26 Literature Study Techniques Testing 4. Which is better?

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Challenges in Testing q Testing prefetching and caching techniques is very expensive q Require real-user traces (i.e., “usage-based test”) q hire participants q obtain permission q provide guidelines q provide compensation q … 27

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Usage-based Test Background q Emerging research area in Software Testing (since 2018) q State-of-the-art research: test-reuse techniques 29

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FrUITeR at a High Level q Goal: find out who’s better q Reproduce existing techniques q Establish standard pipeline q Fair comparison in the same way A Framework for Evaluating UI Test Reuse 30

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Challenges in Practice Discuss with authors 31 Study implementation Modify implementation Establish benchmark Construct ground truths

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Reproducibility Crisis 32 Common Theme q “It’s hard to (re)use other research tools” q “They don’t have documentation” q “It’s hard to run a research tool on my own apps” q …

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33 FrUITeR’s Open Science Contribution FrUITeR’s Website: https://felicitia.github.io/FrUITeR/

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v Prefetching & caching is good idea J encouraged more techniques Recap Dissertation Contributions 34

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v Prefetching & caching is good idea J encouraged more techniques v First content-based (PALOMA) inspired follow-up work Recap Dissertation Contributions 35

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v Prefetching & caching is good idea J encouraged more techniques v First content-based (PALOMA) inspired follow-up work v Make history-based practical (HiPHarness) challenged prior conclusion & re-opened this area Recap Dissertation Contributions 36

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4. Which is better? (FrUITeR) Dissertation & Beyond Mobile-App Performance 38 Software Testing Open Science

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Dissertation Publications v Literature Review & Empirical Study [ASE 2018] v Prefetching & Caching Techniques [MOBILESoft 2017 SRC ICSE 2018, MOBILESoft 2021] v Software Testing [ESEC/FSE 2020] v Open Science [MOBILESoft 2019 Visions, ICSE 2019 Doctoral Symposium] 39