AppInsight: Mobile App Performance Monitoring in the Wild

220 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “, for understanding software behavior or bugs, software performance or resource use, or for other software improvements [13, 39, 46, 59, 61, 77].”
PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, methodology): “These analyses have been used for analyzing performance [26, 27, 15], bugs [22, 25, 19], privacy and security [11, 24], compliance [20] and correctness [18], of apps, some at a scale of thousands of apps.”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, background): “, for understanding software behavior or bugs, software performance or resource use, or for other software improvements [13, 39, 46, 59, 61, 77].”
Mahimahi: Accurate Record-and-Replay for HTTP (2015, background): “Similarly, a mobile app developer may wish to determine the user-perceived latency [28] for user interactions over different wireless networks.”
Panappticon: Event-based tracing to measure mobile application and platform performance (2013, background): “Intuitively, the transaction captures the interval between the user instructing the device to do something and the expected result being displayed.”