TY - GEN
T1 - Learning performance optimization from code changes for android apps
AU - Feng, Ruitao
AU - Meng, Guozhu
AU - Xie, Xiaofei
AU - Su, Ting
AU - Liu, Yang
AU - Lin, Shang Wei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Performance issues of Android apps can tangibly degrade user experience. However, it is challenging for Android developers, especially a novice to develop high-performance apps. It is primarily attributed to the lack of consolidated and abundant programmatic guides for performance optimization. To address this challenge, we propose a data-based approach to obtain performance optimization practices from historical code changes. We first elicit performance-aware Android APIs of which invocations could affect app performance to a large extent, identify historical code changes that produce impact on app performance, and further determine whether they are optimization practices. We have implemented this approach with a tool \tool and evaluated its effectiveness in 2 open source well-maintained projects. The experimental results found 83 changes relevant to performance optimization. Last, we summarize and explain 5 optimization rules to facilitate the development of high-performance apps.
AB - Performance issues of Android apps can tangibly degrade user experience. However, it is challenging for Android developers, especially a novice to develop high-performance apps. It is primarily attributed to the lack of consolidated and abundant programmatic guides for performance optimization. To address this challenge, we propose a data-based approach to obtain performance optimization practices from historical code changes. We first elicit performance-aware Android APIs of which invocations could affect app performance to a large extent, identify historical code changes that produce impact on app performance, and further determine whether they are optimization practices. We have implemented this approach with a tool \tool and evaluated its effectiveness in 2 open source well-maintained projects. The experimental results found 83 changes relevant to performance optimization. Last, we summarize and explain 5 optimization rules to facilitate the development of high-performance apps.
KW - Android App
KW - Change Abstraction
KW - Performance Optimization
UR - https://www.scopus.com/pages/publications/85068368145
U2 - 10.1109/ICSTW.2019.00067
DO - 10.1109/ICSTW.2019.00067
M3 - 会议稿件
AN - SCOPUS:85068368145
T3 - Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019
SP - 285
EP - 290
BT - Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019
Y2 - 22 April 2019 through 27 April 2019
ER -