research-article

DiagDroid: Android performance diagnosis via anatomizing asynchronous executions

Authors:

Michael R. LyuAuthors Info & Claims

FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

Pages 410 - 421

https://doi.org/10.1145/2950290.2950316

Published: 01 November 2016 Publication History

Abstract

Rapid UI responsiveness is a key consideration to Android app developers. However, the complicated concurrency model of Android makes it hard for developers to understand and further diagnose the UI performance. This paper presents DiagDroid, a tool specifically designed for Android UI performance diagnosis. The key notion of DiagDroid is that UI-triggered asynchronous executions contribute to the UI performance, and hence their performance and their runtime dependency should be properly captured to facilitate performance diagnosis. However, there are tremendous ways to start asynchronous executions, posing a great challenge to profiling such executions and their runtime dependency. To this end, we properly abstract five categories of asynchronous executions as the building basis. As a result, they can be tracked and profiled based on the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. DiagDroid can then accordingly profile the asynchronous executions in a task granularity, equipping it with low-overhead and high compatibility merits. The tool is successfully applied in diagnosing 33 real-world open-source apps, and we find 14 of them contain 27 performance issues. It shows the effectiveness of our tool in Android UI performance diagnosis. The tool is open-source released online.

References

[1]

S. Alimadadi, A. Mesbah, and K. Pattabiraman. Understanding asynchronous interactions in full-stack JavaScript. In Proc. of ICSE ’16, 2016.

Digital Library

[2]

E. Altman, M. Arnold, S. Fink, and N. Mitchell. Performance analysis of idle programs. In Proc. of OOPSLA ’10, pages 739–753, 2010.

Digital Library

[3]

D. Amalfitano, A. R. Fasolino, P. Tramontana, S. De Carmine, and A. M. Memon. Using gui ripping for automated testing of android applications. In Proc. of ASE ’12, pages 258–261, 2012.

Digital Library

[4]

C. Amrutkar, M. Hiltunen, T. Jim, K. Joshi, O. Spatscheck, P. Traynor, and S. Venkataraman. Why is my smartphone slow? on the fly diagnosis of underperformance on the mobile internet. In Proc. of DSN ’13, pages 1–8, 2013.

Digital Library

[5]

S. Anand, M. Naik, M. J. Harrold, and H. Yang. Automated concolic testing of smartphone apps. In Proc. of FSE ’12, pages 1–11, 2012.

Digital Library

[6]

Apktool: A tool for reverse engineering Android apk files. http://ibotpeaches.github.io/Apktool/.

[7]

AsyncTask. http://developer.android.com/reference/ android/os/AsyncTask.html.

[8]

Automating User Interface Tests. http://developer. android.com/training/testing/ui-testing/index.html.

[9]

T. Azim and I. Neamtiu. Targeted and depth-first exploration for systematic testing of android apps. In Proc. of OOPLSA ’13, pages 641–660, 2013.

Digital Library

[10]

A. Banerjee. Static analysis driven performance and energy testing. In Proc. of FSE ’14, pages 791–794, 2014.

Digital Library

[11]

A. Banerjee, S. Chattopadhyay, and A. Roychoudhury. Static analysis driven cache performance testing. In Proc. of RTSS ’13, pages 319–329, 2013.

Digital Library

[12]

G. Bavota, M. Linares-Vásquez, C. E. Bernal-Cárdenas, M. Di Penta, R. Oliveto, and D. Poshyvanyk. The impact of api change- and fault-proneness on the user ratings of android apps. Software Engineering, IEEE Transactions on, 41(4):384–407, Apr 2015.

Digital Library

[13]

J. Burnim and K. Sen. Asserting and checking determinism for multithreaded programs. In Proc. of FSE ’09, pages 3–12, 2009.

Digital Library

[14]

Y. Cai and L. Cao. Effective and precise dynamic detection of hidden races for java programs. In Proc. of FSE ’15, pages 450–461, 2015.

Digital Library

[15]

Q. A. Chen, H. Luo, S. Rosen, Z. M. Mao, K. Iyer, J. Hui, K. Sontineni, and K. Lau. Qoe doctor: Diagnosing mobile app qoe with automated ui control and cross-layer analysis. In Proc. of IMC ’14, pages 151–164, 2014.

Digital Library

[16]

W. Choi, G. Necula, and K. Sen. Guided GUI testing of android apps with minimal restart and approximate learning. In Proc. of OOPSLA ’13, pages 623–640, 2013.

Digital Library

[17]

S. R. Choudhary, A. Gorla, and A. Orso. Automated test input generation for android: Are we there yet? In Proc. of ASE ’15, pages 429–440, 2015.

Digital Library

[18]

Configuring ART. https://source.android.com/ devices/tech/dalvik/configure.html.

[19]

DiagDroid - Android Performance Diagnosis via Anatomizing Asynchronous Executions. http://www.cudroid.com/DiagDroid.

[20]

D. Engler, D. Y. Chen, S. Hallem, A. Chou, and B. Chelf. Bugs as deviant behavior: A general approach to inferring errors in systems code. In Proc. of SOSP ’01, pages 57–72, 2001.

Digital Library

[21]

F-Droid. https://f-droid.org/.

[22]

FBReader - Favorite Book Reader. https://fbreader.org/.

[23]

Y. Fratantonio, A. Machiry, A. Bianchi, C. Kruegel, and G. Vigna. Clapp: Characterizing loops in android applications. In Proc. of FSE ’15, pages 687–697, 2015.

Digital Library

[24]

G. Gan, C. Ma, and J. Wu. Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability). 2007.

Digital Library

[25]

L. Gomez, I. Neamtiu, T. Azim, and T. Millstein. Reran: Timing- and touch-sensitive record and replay for android. In Proc. of ICSE ’13, pages 72–81, 2013.

Digital Library

[26]

M. S. Gordon, D. K. Hong, P. M. Chen, J. Flinn, S. Mahlke, and Z. M. Mao. Accelerating mobile applications through flip-flop replication. In Proc. of MobiSys ’15, pages 137–150, 2015.

Digital Library

[27]

K. Herzig, M. Greiler, J. Czerwonka, and B. Murphy. The art of testing less without sacrificing quality. In Proc. of ICSE ’15, pages 483–493, 2015.

Digital Library

[28]

J. Huang and L. Rauchwerger. Finding schedule-sensitive branches. In Proc. of FSE ’15, pages 439–449, 2015.

Digital Library

[29]

C. S. Jensen, M. R. Prasad, and A. Møller. Automated testing with targeted event sequence generation. In Proc. of ISSTA ’13, pages 67–77, 2013.

Digital Library

[30]

M. Ji, E. W. Felten, and K. Li. Performance measurements for multithreaded programs. SIGMETRICS Perform. Eval. Rev., 26(1):161–170, June 1998.

Digital Library

[31]

M. Jovic, A. Adamoli, and M. Hauswirth. Catch me if you can: Performance bug detection in the wild. In Proc. of OOPLSA ’11, pages 155–170, 2011.

Digital Library

[32]

V. Kahlon, N. Sinha, E. Kruus, and Y. Zhang. Static data race detection for concurrent programs with asynchronous calls. In Proc. of FSE ’09, pages 13–22, 2009.

Digital Library

[33]

K. Lee, D. Chu, E. Cuervo, J. Kopf, Y. Degtyarev, S. Grizan, A. Wolman, and J. Flinn. Outatime: Using speculation to enable low-latency continuous interaction for mobile cloud gaming. In Proc. of MobiSys ’15, pages 151–165, 2015.

Digital Library

[34]

Y. Lei and R. H. Carver. Reachability testing of concurrent programs. IEEE Trans. Softw. Eng., 32(6):382–403, June 2006.

Digital Library

[35]

Y. Lin, S. Okur, and D. Dig. Study and refactoring of android asynchronous programming. In Proc. of ASE ’15, pages 224–235, 2015.

Digital Library

[36]

Y. Lin, C. Radoi, and D. Dig. Retrofitting concurrency for android applications through refactoring. In Proc. of FSE ’14, pages 341–352, 2014.

Digital Library

[37]

Y.-D. Lin, J. Rojas, E.-H. Chu, and Y.-C. Lai. On the accuracy, efficiency, and reusability of automated test oracles for android devices. Software Engineering, IEEE Transactions on, 40(10):957–970, Oct 2014.

[38]

Y. Liu, C. Xu, and S.-C. Cheung. Characterizing and detecting performance bugs for smartphone applications. In Proc. of ICSE ’14, pages 1013–1024, 2014.

Digital Library

[39]

Y. Liu, C. Xu, and S.-C. Cheung. Diagnosing energy efficiency and performance for mobile internetware applications. Software, IEEE, 32(1):67–75, Jan 2015.

Digital Library

[40]

Low RAM Configuration. https://source.android.com/ devices/tech/config/low-ram.html.

[41]

S. Lu, S. Park, E. Seo, and Y. Zhou. Learning from mistakes: A comprehensive study on real world concurrency bug characteristics. In Proc. of ASPLOS ’08, pages 329–339, 2008.

Digital Library

[42]

A. Machiry, R. Tahiliani, and M. Naik. Dynodroid: An input generation system for android apps. In Proc. of FSE ’13, pages 224–234, 2013.

Digital Library

[43]

R. Mahmood, N. Mirzaei, and S. Malek. Evodroid: Segmented evolutionary testing of android apps. In Proc. of FSE ’14, pages 599–609, 2014.

Digital Library

[44]

N. Mirzaei, S. Malek, C. S. Păsăreanu, N. Esfahani, and R. Mahmood. Testing android apps through symbolic execution. ACM SIGSOFT Software Engineering Notes, 37(6):1–5, Nov. 2012.

Digital Library

[45]

Monkeyrunner. http://developer.android.com/tools/ help/monkeyrunner concepts.html.

[46]

Y. Moon, D. Kim, Y. Go, Y. Kim, Y. Yi, S. Chong, and K. Park. Practicalizing delay-tolerant mobile apps with cedos. In Proc. of MobiSys ’15, pages 419–433, 2015.

Digital Library

[47]

F. F.-H. Nah. A study on tolerable waiting time: how long are web users willing to wait? Behaviour & Information Technology, 23(3):153–163, 2004.

[48]

D. T. Nguyen, G. Zhou, G. Xing, X. Qi, Z. Hao, G. Peng, and Q. Yang. Reducing smartphone application delay through read/write isolation. In Proc. of Mobisys ’15, pages 287–300, 2015.

Digital Library

[49]

J. Nielsen. Usability Engineering. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.

[50]

S. Niida, S. Uemura, and H. Nakamura. Mobile services. IEEE Vehicular Technology Magazine, 5(3):61–67, Sep 2010.

[51]

A. Nistor, P. C. Chang, C. Radoi, and S. Lu. Caramel: Detecting and fixing performance problems that have non-intrusive fixes. In Proc. of ICSE ’15, 2015.

Digital Library

[52]

OpenLaw - Die Gesetze App. https://openlaw.jdsoft.de/.

[53]

Processes and Threads. http://developer.android. com/guide/components/processes-and-threads.html.

[54]

Profiling with Traceview and dmtracedump. http://developer.android.com/tools/debugging/ debugging-tracing.html.

[55]

Z. Qin, Y. Tang, E. Novak, and Q. Li. Mobiplay: A remote execution based record-and-replay tool for mobile applications. In Proc. of ICSE ’16, 2016.

Digital Library

[56]

L. Ravindranath, S. Nath, J. Padhye, and H. Balakrishnan. Automatic and scalable fault detection for mobile applications. In Proc. of Mobisys ’14, pages 190–203, 2014.

Digital Library

[57]

L. Ravindranath, J. Padhye, S. Agarwal, R. Mahajan, I. Obermiller, and S. Shayandeh. Appinsight: Mobile app performance monitoring in the wild. In Proc. of OSDI ’12, pages 107–120, 2012.

Digital Library

[58]

L. Ravindranath, J. Padhye, R. Mahajan, and H. Balakrishnan. Timecard: Controlling user-perceived delays in server-based mobile applications. In Proc. of SOSP ’13, pages 85–100, 2013.

Digital Library

[59]

RobotiumTech. Robotium: User scenario testing for Android. http://www.robotium.org.

[60]

V. Roto and A. Oulasvirta. Need for non-visual feedback with long response times in mobile hci. In Proc. of WWW ’05, pages 775–781, 2005.

Digital Library

[61]

A. Sarkar, J. Guo, N. Siegmund, S. Apel, and K. Czarnecki. Cost-efficient sampling for performance prediction of configurable systems. In Proc. of ASE ’15, pages 342–352, 2015.

Digital Library

[62]

M. Selakovic and M. Pradel. Performance issues and optimizations in JavaScript: An empirical study. In Proc. of ICSE ’16, 2016.

Digital Library

[63]

StrictMode. http://developer.android.com/reference/ android/os/StrictMode.html.

[64]

V. Terragni, S.-C. Cheung, and C. Zhang. Recontest: Effective regression testing of concurrent programs. In Proc. of ICSE ’15, pages 246–256, 2015.

Digital Library

[65]

Testing Support Library - UI Automator. https://developer.android.com/tools/ testing-support-library/index.html#UIAutomator.

[66]

R. A. to search for countries based on several parameters. https://github.com/abhi2rai/RestC.

[67]

UI/Application Exerciser Monkey. http: //developer.android.com/tools/help/monkey.html.

[68]

A. Wert, J. Happe, and L. Happe. Supporting swift reaction: Automatically uncovering performance problems by systematic experiments. In Proc. of ICSE ’13, pages 552–561, 2013.

Digital Library

[69]

Xposed Module overview. http://repo.xposed.info/module-overview.

[70]

Xposed Module Repository. http://repo.xposed.info.

[71]

X. Yu, S. Han, D. Zhang, and T. Xie. Comprehending performance from real-world execution traces: A device-driver case. SIGPLAN Not., 49(4):193–206, Feb. 2014.

Digital Library

[72]

L. Zhang, D. R. Bild, R. P. Dick, Z. M. Mao, and P. Dinda. Panappticon: Event-based tracing to measure mobile application and platform performance. In Proc. of CODES+ISSS ’13, pages 1–10, 2013.

Digital Library

[73]

Y. Zhang, J. Guo, E. Blais, and K. Czarnecki. Performance prediction of configurable software systems by fourier learning. In Proc. of ASE ’15, pages 365–373, 2015.

Digital Library

Cited By

Li WMa JJiang YXu CMa X(2024)Understanding and Detecting Inefficient Image Displaying Issues in Android AppsJournal of Computer Science and Technology10.1007/s11390-022-1670-339:2(434-459)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11390-022-1670-3
Cui BWang MZhang CYan JYan JZhang J(2023)Detection of Java Basic Thread Misuses Based on Static Event Analysis2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00145(1049-1060)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00145
Tang YWang HZhan XLuo XZhou YZhou HYan QSui YKeung J(2022)A Systematical Study on Application Performance Management Libraries for AppsIEEE Transactions on Software Engineering10.1109/TSE.2021.307765448:8(3044-3065)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3077654
Show More Cited By

Index Terms

DiagDroid: Android performance diagnosis via anatomizing asynchronous executions
1. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

Recommendations

Android: Changing the Mobile Landscape

The mobile phone landscape changed last year with the introduction of smart phones running Android, a platform marketed by Google. Android phones are the first credible threat to the iPhone market. Not only did Google target the same consumers as iPhone,...
Inter-app communication between Android apps developed in app-inventor and Android studio
MOBILESoft '16: Proceedings of the International Conference on Mobile Software Engineering and Systems

Communications between mobile apps are an important aspect of mobile platforms. Android is specifically designed with inter-app communication in mind and depends on this to provide different platform specific functionalities. Android Apps can either be ...
DEMO: Enabling trusted stores for android
CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security

In the Android ecosystem, the process of verifying the integrity of downloaded apps is left to the user. Different from other systems, e.g., Apple App Store, Google does not provide any certified vetting process for the Android apps. This choice has a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

November 2016

1156 pages

ISBN:9781450342186

DOI:10.1145/2950290

General Chair:
Thomas Zimmermann
Microsoft Research, USA
,
Program Chairs:
Jane Cleland-Huang
University of Notre Dame, USA
,
Zhendong Su
University of California at Davis, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Research Grants Council of the Hong Kong Special Administrative Region
2015 Microsoft Research Asia Collaborative Research Program
National Basic Research Program of China

Conference

FSE'16

Sponsor:

SIGSOFT

FSE'16: 24nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering

November 13 - 18, 2016

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 17 of 128 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
601
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)3

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li WMa JJiang YXu CMa X(2024)Understanding and Detecting Inefficient Image Displaying Issues in Android AppsJournal of Computer Science and Technology10.1007/s11390-022-1670-339:2(434-459)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11390-022-1670-3
Cui BWang MZhang CYan JYan JZhang J(2023)Detection of Java Basic Thread Misuses Based on Static Event Analysis2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00145(1049-1060)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00145
Tang YWang HZhan XLuo XZhou YZhou HYan QSui YKeung J(2022)A Systematical Study on Application Performance Management Libraries for AppsIEEE Transactions on Software Engineering10.1109/TSE.2021.307765448:8(3044-3065)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3077654
Hort MKechagia MSarro FHarman M(2022)A Survey of Performance Optimization for Mobile ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2021.307119348:8(2879-2904)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3071193
Fu JWang YZhou YWang X(2022)How resource utilization influences UI responsiveness of Android softwareInformation and Software Technology10.1016/j.infsof.2021.106728141:COnline publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.infsof.2021.106728
Lei ZZhao WDing ZXia MQi Z(2022)AppSPIN: reconfiguration-based responsiveness testing and diagnosing for Android AppsAutomated Software Engineering10.1007/s10515-022-00347-929:2Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10515-022-00347-9
Song WHan MHuang J(2021)IMGDroidProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00080(823-834)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00080
Li WJiang YMa JXu C(2021)Automatic Performance Testing for Image Displaying in Android Apps2021 28th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC53868.2021.00039(317-326)Online publication date: Dec-2021
https://doi.org/10.1109/APSEC53868.2021.00039
Pan LCui BLiu HYan JWang SYan JZhang JDevanbu PCohen MZimmermann T(2020)Static asynchronous component misuse detection for Android applicationsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409699(952-963)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409699
Sanchez ADelgado-Perez PMedina-Bulo ISegura S(2020)TANDEM: A Taxonomy and a Dataset of Real-World Performance BugsIEEE Access10.1109/ACCESS.2020.30009288(107214-107228)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3000928
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten