Hang doctor: runtime detection and diagnosis of soft hangs for smartphone apps

Abstract

A critical quality factor for smartphone apps is responsiveness, which indicates how fast an app reacts to user actions. A soft hang occurs when the app's response time of handling a certain user action is longer than a user-perceivable delay. Soft hangs can be caused by normal User Interface (UI) rendering or some blocking operations that should not be conducted on the app's main thread (i.e., soft hang bugs). Existing solutions on soft hang bug detection focus mainly on offline app code examination to find previously known blocking operations and then move them off the main thread. Unfortunately, such offline solutions can fail to identify blocking operations that are previously unknown or hidden in libraries. In this paper, we present Hang Doctor, a runtime methodology that supplements the existing offline algorithms by detecting and diagnosing soft hangs caused by previously unknown blocking operations. Hang Doctor features a two-phase algorithm that first checks response time and performance event counters for detecting possible soft hang bugs with small overheads, and then performs stack trace analysis when diagnosis is necessary. A novel soft hang filter based on correlation analysis is designed to minimize false positives and negatives for high detection performance and low overhead. We have implemented a prototype of Hang Doctor and tested it with the latest releases of 114 real-world apps. Hang Doctor has identified 34 new soft hang bugs that are previously unknown to their developers, among which 62%, so far, have been confirmed by the developers, and 68% are missed by offline algorithms.