A framework to assess Android mobile applications energy consumption and privacy policies.

为什么我的手机电量已经很低了？怎么会发生我每月电话套餐的数据几乎全部用完的情况呢？为什么新的应用程序要跟踪我的位置？这些都是不明显的问题。选择应用程序需要努力、耐心来检查相关信息和技术知识。事实上，最好的应用组合取决于用户偏好、不同功能的使用频率、单一功能的性能、用户隐私问题以及应用(隐藏)成本。如果我们考虑Android生态系统，对于任何给定的应用程序类别(例如，电子邮件客户端、天气预报、商业、通信、工具、游戏)，用户有大量的不同应用程序的选择(通常实现相同或非常相似的功能)。然而，尽管一个应用程序类别列出了数十个应用程序，但提供的信息通常仅限于特定应用程序的广告、Android市场元数据和应用程序排名。在设置Android手机环境时，用户必须尝试不同的应用程序，验证已实现的功能，评估广告的侵入性，考虑不同应用程序的利弊，希望在这样做的过程中不会泄露个人或机密信息。 这项研究计划旨在开发一个支持用户和开发人员的框架。我们的目标是支持用户根据用户需求选择最佳的应用程序集，并支持开发人员设计和实现高效且节能的应用程序。具体来说，我们将支持对移动应用程序的分析、评估和验证：1)指定场景的能耗；2)应用程序实现的功能(和功能特定的能耗)；3)应用程序隐私实施的政策。 在短期内，我们将向用户和选定的开发人员提供Web门户。用户将能够选择一些应用程序类别，设置他们的能源、网络和保密问题(高、中、低或不关心)，指定他们愿意为应用程序提供的授权列表，并自动获得要安装在他们手机上的最佳或接近最佳的应用程序列表。 我们将使用场景定义语言来概括这一点。此外，我们将为选定的开发人员提供上传应用程序、选择场景(如果需要，定义应用程序特定场景)和生成应用程序能耗配置文件的可能性。 从长远来看，我们的目标是扩展工具链，支持1)混合语言应用程序(原生C和Java或纯C/C++应用程序)；2)应用程序和应用程序执行场景共同演进；3)不同的硬件和Android版本。这些都是不平凡和具有挑战性的目标。不同的硬件意味着不同的屏幕几何形状、设备驱动程序和Android API。应用程序和Android演进可能会极大地改变用户和应用程序的交互方式：场景、隐私策略和收集信息的设置，从而导致应用程序和场景共同进化的需求。

Why is my cell phone battery already low? How did it happen that almost all the data of my monthly phone plan is already used up? Why is the new app tracking my location? These are non-obvious questions. Choosing apps requires effort, patience to examine pertinent information, and technical knowledge. In fact, the best app combination depends on user preference, frequency of use of different features, single feature performance, user privacy concerns, and app (hidden) costs. If we consider the Android ecosystem, for any given app category (e.g., e-mail clients, weather forecast, business, communication, tools, games), the user has plenty of choices for different apps (often implementing the same, or very similar, features). However, quite often, while an app category lists dozens of apps, the information supplied is limited to the advertisement for a specific app, the Android market meta-data, and the app ranking. When setting up an Android phone environment, the user has to try different apps, verify implemented features, evaluate intrusiveness of advertisements, ponder pros and cons of different apps, hoping that, in doing so, no personal or confidential information is leaked. This research program is directed to the development of a framework that will support users and developers. We aim at supporting users in selecting the best set of apps, given the user needs, and supporting developers in designing and implementing efficient and energy aware apps. Specifically, we will support the analysis, assessment and verification of mobile apps’ 1) energy consumption per specified scenario; 2) app implemented features (and feature specific energy consumption); and 3) app privacy implemented policies. In the short term, we will make available a WEB portal to users and selected developers. Users will be able to select some app categories, set their energy, network, and confidentiality concerns (high, medium, low or don’t care), specify the list of grants they are willing to give to apps, and, automatically, obtain a list of optimal, or close to optimal, apps to be installed in their phones. We will generalize this with a scenario definition language. Also, we will offer selected developers the possibility to upload an app, select a scenario (define an app specific scenario if needed), and produce an app energy consumption profile. In the long term, we aim at extending the tool chain supporting 1) mixed language apps (native C and Java or pure C/C++ apps); 2) app and app execution scenarios co-evolution; and 3) different hardware and Android versions. These are all non-trivial and challenging goals. Different hardware implies different screen geometry, device drivers and Android API. App and Android evolution may substantially modify the way in which the user and the app interact: scenarios, privacy policies, and setup to gather information leading to the need of app and scenarios co-evolution.