检测移动应用程序中的能量漏洞和热点

Abhijeet Banerjee, Lee Kee Chong, Sudipta Chattopadhyay, Abhik Roychoudhury
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引用次数: 193

摘要

近年来,智能手机的普及程度急剧上升。这导致了智能手机应用程序的广泛可用性。由于智能手机的电池电量有限,因此开发有助于节能应用程序开发的工具和技术非常重要。智能手机应用中的能源效率低下可以大致分为能源热点和能源漏洞。能源热点可以描述为这样一种情况,即执行应用程序会导致智能手机消耗异常高的电池电量,尽管其硬件资源的利用率很低。相比之下,能量漏洞可以描述为这样一种情况,即出现故障的应用程序阻止智能手机处于空闲状态,即使它已经完成执行并且没有用户活动。在本文中,我们提出了一个自动测试生成框架,用于检测Android应用程序中的能源热点/错误。我们的框架系统地生成可能捕获能量热点/错误的测试输入。每个测试输入捕获一系列用户交互(例如,在智能手机屏幕上触摸或点击),从而导致应用程序中的能量热点/错误。通过对Google Play/F-Droid上30款免费Android应用的评估,我们发现了我们的框架在寻找热点/漏洞方面的有效性。手工验证实验结果表明,我们的框架报告的误报数量相当低。最后,我们通过改进一些Android应用程序的能效来展示生成结果的使用情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting energy bugs and hotspots in mobile apps
Over the recent years, the popularity of smartphones has increased dramatically. This has lead to a widespread availability of smartphone applications. Since smartphones operate on a limited amount of battery power, it is important to develop tools and techniques that aid in energy-efficient application development. Energy inefficiencies in smartphone applications can broadly be categorized into energy hotspots and energy bugs. An energy hotspot can be described as a scenario where executing an application causes the smartphone to consume abnormally high amount of battery power, even though the utilization of its hardware resources is low. In contrast, an energy bug can be described as a scenario where a malfunctioning application prevents the smartphone from becoming idle, even after it has completed execution and there is no user activity. In this paper, we present an automated test generation framework that detects energy hotspots/bugs in Android applications. Our framework systematically generates test inputs that are likely to capture energy hotspots/bugs. Each test input captures a sequence of user interactions (e.g. touches or taps on the smartphone screen) that leads to an energy hotspot/bug in the application. Evaluation with 30 freely-available Android applications from Google Play/F-Droid shows the efficacy of our framework in finding hotspots/bugs. Manual validation of the experimental results shows that our framework reports reasonably low number of false positives. Finally, we show the usage of the generated results by improving the energy-efficiency of some Android applications.
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