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AppInsight: Mobile App Performance Monitoring in the Wild

Lenin Ravindranath, Jitendra Padhye, Sharad Agarwal, Ratul Mahajan, Ian Obermiller, Shahin Shayandeh
2012 USENIX Symposium on Operating Systems Design and Implementation  
The mobile-app marketplace is highly competitive. To maintain and improve the quality of their apps, developers need data about how their app is performing in the wild.  ...  The asynchronous, multi-threaded nature of mobile apps makes tracing difficult. The difficulties are compounded by the resource limitations inherent in the mobile platform.  ...  We also thank Petros Maniatis and the anonymous reviewers for their comments on earlier drafts of this paper.  ... 
dblp:conf/osdi/RavindranathPAMOS12 fatcat:65ka52katrah5b7ye43yb3lrzi

Automatic and scalable fault detection for mobile applications

Lenin Ravindranath, Suman Nath, Jitendra Padhye, Hari Balakrishnan
2014 Proceedings of the 12th annual international conference on Mobile systems, applications, and services - MobiSys '14  
This paper describes the design, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications ("apps").  ...  The techniques in VanarSena are driven by a study of 25 million real-world crash reports of Windows Phone apps reported in 2012.  ...  LR and HB were supported in part by the National Science Foundation under Grant 0931508 and the MIT Center for Wireless Networks and Mobile Computing (Wireless@MIT).  ... 
doi:10.1145/2594368.2594377 dblp:conf/mobisys/RavindranathNPB14 fatcat:3pmwijkftndmjlezcyltq5liee

SIF

Shuai Hao, Ding Li, William G.J. Halfond, Ramesh Govindan
2013 Proceeding of the 11th annual international conference on Mobile systems, applications, and services - MobiSys '13  
Mobile app ecosystems have experienced tremendous growth in the last five years.  ...  SIF's overhead is under 2% in most cases, and its instrumentation overhead feedback is within 15% in many cases. As such, we expect that SIF can accelerate studies of the mobile app ecosystem.  ...  Morley Mao, and the anonymous reviewers, for their insightful suggestions for improving the technical content and presentation of the paper. REFERENCES  ... 
doi:10.1145/2462456.2465430 dblp:conf/mobisys/HaoLHG13 fatcat:c6nu2p6o5fffdfw7yu3fwsd2qi

Reproducing context-sensitive crashes of mobile apps using crowdsourced monitoring

María Gómez, Romain Rouvoy, Bram Adams, Lionel Seinturier
2016 Proceedings of the International Workshop on Mobile Software Engineering and Systems - MOBILESoft '16  
This paper introduces MoTiF, a crowdsourced approach to support app developers in automatically reproducing contextsensitive crashes faced by end-users in the wild.  ...  While the number of mobile apps published by app stores keeps on increasing, the quality of these apps varies widely.  ...  AppInsight [38] is a system to monitor app performance in the wild for the Windows Phone platform.  ... 
doi:10.1145/2897073.2897088 dblp:conf/icse/GomezRAS16 fatcat:6ri7eoxxpfh25nebgmhcwgizci

Caiipa

Chieh-Jan Mike Liang, Ranveer Chandra, Feng Zhao, Nicholas D. Lane, Niels Brouwers, Li Zhang, Börje F. Karlsson, Hao Liu, Yan Liu, Jun Tang, Xiang Shan
2014 Proceedings of the 20th annual international conference on Mobile computing and networking - MobiCom '14  
The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way.  ...  Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).  ...  First, apps are exposed to a large-scale library of diverse contexts synthesized from the actual conditions observed in the wild.  ... 
doi:10.1145/2639108.2639131 dblp:conf/mobicom/LiangLBZKLLTSCZ14 fatcat:wonw54qb2ndqnosvmnsuua7l54

Mining test repositories for automatic detection of UI performance regressions in Android apps

María Gómez, Romain Rouvoy, Bram Adams, Lionel Seinturier
2016 Proceedings of the 13th International Workshop on Mining Software Repositories - MSR '16  
The main research challenge of automatically identifying UI performance problems on mobile devices is that the performance of an app highly varies depending on its context-i.e., the hardware and software  ...  The reputation of a mobile app vendor is crucial to survive amongst the ever increasing competition.  ...  AppInsight [34] is a system to monitor app performance in the wild for the Windows Phone platform.  ... 
doi:10.1145/2901739.2901747 dblp:conf/msr/GomezRAS16 fatcat:sptdjuyhsvbz3p2r2ijytbsziy

Smartphone Energy Drain in the Wild

Xiaomeng Chen, Ning Ding, Abhilash Jindal, Y. Charlie Hu, Maruti Gupta, Rath Vannithamby
2015 Performance Evaluation Review  
Second, through analyzing traces collected on 1520 Galaxy S3 and S4 devices in the wild, we present detailed analysis of where the CPU time and energy is spent across the 1520 devices, inside the 800 apps  ...  In this paper, we conduct the first extensive measurement and modeling of energy drain of 1520 smartphone in the wild. We make two primary contributions.  ...  to sta- [20] monitors the performance of mobile apps in the wild by instrumenting app binary.  ... 
doi:10.1145/2796314.2745875 fatcat:tsd3awp6pratdjb4chi7b4cem4

A Systematical Study on Application Performance Management Libraries for Apps [article]

Yutian Tang, Haoyu Wang, Xian Zhan, Xiapu Luo, Yajin Zhou, Hao Zhou, Qiben Yan, Yulei Sui, Jacky Keung
2021 arXiv   pre-print
Application Performance Management (APM) libraries are used to locate the apps' performance bottleneck, monitor their behaviors at runtime, and identify potential security risks.  ...  Being able to automatically detect the performance issues in apps can significantly improve apps' quality as well as having a positive influence on user satisfaction.  ...  AppInsight [91] instrumented mobile apps by interposing event handlers to collect information on critical paths that are triggered by user transactions. Lee et al.  ... 
arXiv:2103.11286v1 fatcat:awmwixfosfbuvhx24ci6cjagme

Capturing mobile experience in the wild

Ashish Patro, Shravan Rayanchu, Michael Griepentrog, Yadi Ma, Suman Banerjee
2013 Proceedings of the ninth ACM conference on Emerging networking experiments and technologies - CoNEXT '13  
We present a long term and large scale study of the experience of mobile users through two popular but contrasting applications in the wild.  ...  To conduct this study, we implemented a measurement framework and library, called Insight, which has been deployed on these two applications that are available through Apple's App Store and Google's Android  ...  ACKNOWLEDGMENTS We thank the application developers involved in this study for their support, without whom this work would not have been possible.  ... 
doi:10.1145/2535372.2535391 dblp:conf/conext/PatroRGMB13 fatcat:g2u4oupqnzbodkulkuvddfdvre

Procrastinator

Lenin Ravindranath, Sharad Agarwal, Jitendra Padhye, Chris Riederer
2014 Proceedings of the 12th annual international conference on Mobile systems, applications, and services - MobiSys '14  
Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app.  ...  A typical example is shown in Figure 1 . This popular weather app downloads a large amount of data as soon as the app is  ...  Acknowledgments We thank our shepherd, Ulas Kozat, and the ACM MobiSys 2014 reviewers for their feedback on our work.  ... 
doi:10.1145/2594368.2594387 dblp:conf/mobisys/RavindranathAPR14 fatcat:ay36gkoyajebjchyt6xlegsqlq

DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps

Bin Liu, Suman Nath, Ramesh Govindan, Jie Liu
2014 Symposium on Networked Systems Design and Implementation  
We have implemented DECAF for Windows-based mobile platforms, and applied it to 1,150 tablet apps and 50,000 phone apps in order to characterize the prevalence of ad frauds.  ...  Ad networks for mobile apps require inspection of the visual layout of their ads to detect certain types of placement frauds.  ...  We thank the anonymous referees and our shepherd Aruna Balasubramanian for their comments. Michael Albrecht, Rich Chapler and Navneet Raja provided valuable feedback on DECAF.  ... 
dblp:conf/nsdi/0004NGL14 fatcat:giyby7uzojeq3pokok2gn2wgqm

Argus: Debugging Performance Issues in Modern Desktop Applications with Annotated Causal Tracing

Lingmei Weng, Peng Huang, Jason Nieh, Junfeng Yang
2021 USENIX Annual Technical Conference  
We present Argus, a fast, effective causal tracing tool for debugging performance anomalies in desktop applications.  ...  Although prior work has used causal tracing for debugging performance issues in distributed systems, we find that these techniques suffer from high inaccuracies for desktop applications.  ...  Acknowledgments We thank our shepherd, Pedro Fonseca, and the anonymous reviewers for their valuable feedback.  ... 
dblp:conf/usenix/WengHNY21 fatcat:dsancsapubdjleei2g5zsxgdju

A systematic mapping study of mobile application testing techniques

Samer Zein, Norsaremah Salleh, John Grundy
2016 Journal of Systems and Software  
We performed a systematic mapping study to categorize and to structure the research evidence that has been published in the area of mobile application testing techniques and challenges that they have reported  ...  This is due to the fact that mobile applications are different than traditional web and desktop applications, and more and more they are moving to being used in critical domains.  ...  AppInsight: mobile app performance monitoring in the wild, Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation.  ... 
doi:10.1016/j.jss.2016.03.065 fatcat:gi7ctvbs3jeshidovg737xn44y

Race detection for event-driven mobile applications

Chun-Hung Hsiao, Jie Yu, Satish Narayanasamy, Ziyun Kong, Cristiano L. Pereira, Gilles A. Pokam, Peter M. Chen, Jason Flinn
2014 SIGPLAN notices  
In this paper we present a race detection tool named CAFA for event-driven mobile systems. CAFA uses the causality model that we have developed for the Android event-driven system.  ...  A novel contribution of our model is that it accounts for the causal order due to the event queues, which are not accounted for in past data race detectors.  ...  Acknowledgments We thank the anonymous reviewers for comments that improved this paper. This work is supported by the National Science Foundation CAREER program and Intel, Inc.  ... 
doi:10.1145/2666356.2594330 fatcat:qlvl552ourhxbmvuvpcdqwv6pe

Race detection for event-driven mobile applications

Chun-Hung Hsiao, Cristiano L. Pereira, Jie Yu, Gilles A. Pokam, Satish Narayanasamy, Peter M. Chen, Ziyun Kong, Jason Flinn
2013 Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI '14  
In this paper we present a race detection tool named CAFA for event-driven mobile systems. CAFA uses the causality model that we have developed for the Android event-driven system.  ...  A novel contribution of our model is that it accounts for the causal order due to the event queues, which are not accounted for in past data race detectors.  ...  Acknowledgments We thank the anonymous reviewers for comments that improved this paper. This work is supported by the National Science Foundation CAREER program and Intel, Inc.  ... 
doi:10.1145/2594291.2594330 dblp:conf/pldi/HsiaoPYPNCKF14 fatcat:ps7mg6coebbkjoq3fjyih24p24