作者: Yutian Tang , Haoyu Wang , Xian Zhan , Xiapu Luo , Yajin Zhou
DOI:
关键词: Quality (business) 、 User satisfaction 、 Application performance management 、 Empirical research 、 Android (operating system) 、 Bottleneck 、 Computer science 、 Data science
摘要: Being able to automatically detect the performance issues in apps can significantly improve apps' quality as well having a positive influence on user satisfaction. Application Performance Management (APM) libraries are used locate bottleneck, monitor their behaviors at runtime, and identify potential security risks. Although app developers have been exploiting application management tools capture these issues, most of them do not fully understand internals APM effect apps. To fill this gap, paper, we conduct first systematic study APMs for by scrutinizing 25 widely-used Android develop framework named APMHunter exploring usage Using APMHunter, large-scale empirical 500,000 explore patterns discover misuses APMs. We obtain two major findings: 1) some still employ deprecated permissions approaches, which makes fail perform expected; 2) inappropriate use cause privacy leaks. Thus, our suggests that both vendors should design scrupulously.