SunCat: helping developers understand and predict performance problems in smartphone applications

Abstract

The number of smartphones shipped in 2014 will be four times larger than the number of PCs. Compared to PCs, smartphones have limited computing resources, and smartphone applications are more prone to performance problems. Traditionally, developers use profilers to detect performance problems by running applications with relatively large inputs. Unfortunately, for smartphone applications, the developer cannot easily control the input, because smartphone applications interact heavily with the environment. Given a run on a small input, how can a developer detect performance problems that would occur for a run with large input? We present SUNCAT, a novel technique that helps developers understand and predict performance problems in smartphone applications. The developer runs the application using a common input, typically small, and SUNCAT presents a prioritized list of repetition patterns that summarize the current run plus additional information to help the developer understand how these patterns may grow in the future runs with large inputs. We implemented SUNCAT for Windows Phone systems and used it to understand the performance characteristics of 29 usage scenarios in 5 popular applications. We found one performance problem that was confirmed and fixed, four problems that were confirmed, one confirmed problem that was a duplicate of an older report, and three more potential performance problems that developers agree may be improved.