Mobile platforms are rapidly and continuously changing, with support for new sensors, APIs, and programming abstractions. Static analysis is gaining a growing interest, allowing developers to predict properties about the run-time behavior of mobile apps without executing them. Over the years, literally hundreds of static analysis techniques have been proposed, ranging from structural and control-flow analysis to state-based analysis.In this paper, we present a systematic mapping study aimed at identifying, evaluating and classifying characteristics, trends and potential for industrial adoption of existing research in static analysis of mobile apps. Starting from over 12,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 261 primary studies along a time span of 9 years. We analyzed each primary study according to a rigorously-defined classification framework. The results of this study give a solid foundation for assessing existing and future approaches for static analysis of mobile apps, especially in terms of their industrial adoptability.Researchers and practitioners can use the results of this study to (i) identify existing research/technical gaps to target, (ii) understand how approaches developed in academia can be successfully transferred to industry, and (iii) better position their (past and future) approaches for static analysis of mobile apps.

中文翻译：

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用于静态分析移动应用程序的软件工程技术：研究趋势、特征和工业采用的潜力

移动平台正在快速且持续地变化，支持新的传感器、API 和编程抽象。静态分析越来越受到关注，它允许开发人员在不执行移动应用程序的情况下预测有关移动应用程序运行时行为的属性。多年来，已经提出了数百种静态分析技术，从结构和控制流分析到基于状态的分析。在本文中，我们提出了一个系统的映射研究，旨在识别、评估和分类特征、趋势和潜力用于移动应用程序静态分析中现有研究的工业采用。从超过 12,000 项可能相关的研究开始，我们应用了严格的选择程序，在 9 年的时间跨度内产生了 261 项主要研究。我们根据严格定义的分类框架分析了每项主要研究。这项研究的结果为评估移动应用程序静态分析的现有和未来方法奠定了坚实的基础，尤其是在其行业可采用性方面。研究人员和从业者可以利用这项研究的结果 (i) 确定现有的研究/技术差距目标，(ii) 了解如何将学术界开发的方法成功转移到工业界，以及 (iii) 更好地定位他们（过去和未来）的移动应用静态分析方法。