SHF: Small: Natural GUI-Based Testing of Mobile Apps via Mining Software Repositories

移动设备已经成为现代社会不可或缺的、无处不在的一部分。智能手机和平板电脑的流行在很大程度上要归功于移动软件的成功，这些软件通俗地被称为“应用”，使用户能够以直观和方便的方式执行广泛的计算任务。快速发展的高性能硬件和软件平台支持日益复杂的功能，推动了蓬勃发展的移动应用市场。为了让应用程序在苹果的App Store或Google Play等市场上取得成功，它们的功能必须达到预期，因此必须经过良好的测试。然而，移动应用程序受欢迎的独特方面，如基于触摸的界面、快速发展的平台和传感器等上下文功能，使得它们很难进行有效和高效的测试。此外，随着移动应用市场的成熟，开发者必须确保他们的应用在各种设备上都能很好地运行，同时通过应用商店评论来处理越来越多的用户群的反馈。这些挑战表明，移动开发人员需要实用的自动化支持，以确保他们的应用程序经过充分测试。这项研究项目旨在设计并彻底验证一种针对移动应用程序的自动化测试方法，以克服上面列出的挑战。反过来，预计这项研究带来的技术将有助于更好地测试、更高质量的移动应用程序，使我们越来越依赖智能手机应用程序的社会以及开发这些应用程序的开发人员和团队都受益。为了解决这些根本挑战，该项目旨在开发一个自动化测试框架，将移动应用程序的新颖统计表示法与通过挖掘软件库技术收集的信息相结合，以高效地生成实用、有效的测试场景。更具体地说，将开发一种新的测试框架，称为T。T植根于移动应用程序的基于概率模型的表示。该模型将支持一种变革性的自动化方法来生成可行的测试用例，这些测试用例与底层事件分离，可以在不同的设备上执行，并支持多个测试目标和充分性标准。此外，这项研究工作将定义和开发监测机制，以确定基础平台和第三方库中容易发生变化和故障的API，以及提供信息的审查。将此信息合并到T的统计模型中将允许生成覆盖这些API和审查的测试用例并确定其优先级。这项工作的更广泛影响将在于：(1)改善移动应用程序测试实践的状况，在确保应用程序针对变化的平台、API、评论和众多设备进行充分测试方面面临困难；(2)与行业合作伙伴展示改进的测试实践，这将被记录为其他开发组织和测试中心采用的最佳实践；(3)开发教育课程内容，并将其作为本研究项目的一部分在课堂上进行试点；这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Mobile devices have become an integral, ubiquitous part of modern society. The popularity of smartphones and tablets is largely due to the success of mobile software, colloquially referred to as "apps", that enable users to carry out a wide range of computing tasks in an intuitive and convenient manner. The burgeoning mobile app market is fueled by rapidly evolving performant hardware and software platforms that support increasingly complex functionality. In order for apps to achieve success in marketplaces such as Apple's App Store or Google Play, it is imperative that they function as intended and thus must be well tested. However, the unique aspects of mobile apps that make them popular, such as their touch-based interfaces, rapidly evolving platforms, and contextual features such as sensors, make them difficult to test effectively and efficiently. Additionally, as the marketplace for mobile apps matures, developers must ensure that their apps function well across a myriad of devices while addressing feedback from an increasingly large user base through app store reviews. These challenges illustrate that mobile developers require practical automated support to ensure that their apps are adequately tested. This research project aims to design, and thoroughly validate an automated testing approach for mobile apps that overcomes the challenges listed above. In turn, it is anticipated that the techniques enabled by this research will contribute to better-tested, higher quality mobile applications, benefiting both our society that increasingly depends on smartphone apps and the developers and teams that create them. To solve these fundamental challenges, this project aims to develop an automated testing framework that combines novel statistical representations of mobile apps and information gleaned via mining software repositories techniques to efficiently generate practical, effective test scenarios. More specifically, a novel testing framework, coined as T+, will be developed. T+ is rooted in a probabilistic model-based representation of mobile apps. This model will enable a transformative automated approach for generating feasible test cases that are decoupled from low level events, can be executed on different devices, and support multiple testing goals and adequacy criteria. Additionally, this research work will define and develop monitoring mechanisms for identifying change- and fault- prone APIs in underlying platform and third-party libraries, as well as informative reviews. Incorporation of this information into the statistical model of T+ will allow for the generation and prioritization of test cases covering these APIs and reviews. Broader impacts of this work will reside in (1) improving the state of the practice in testing mobile apps, where difficulties are faced in ensuring that apps are adequately tested with respect to changing platforms, APIs, reviews, and numerous devices; (2) demonstrating improved testing practices with industry partners, which will be documented as best practices for other development organizations and test centers to adopt; (3) developing educational course content and piloting it in the classroom as part of this research project; and (4) actively involving underrepresented categories of students in this research program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.