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

SHF：小型：通过挖掘软件存储库对移动应用程序进行基于 GUI 的自然测试

• 批准号: 1815186
• 负责人: Denys Poshyvanyk
• 金额: $ 45万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815186&HistoricalAwards=false
• 关键词: SHF; Small; Natural; GUI; Based

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.

移动设备已成为现代社会不可或缺的一部分。智能手机和平板电脑的受欢迎程度很大程度上是由于移动软件的成功，该软件被称为“应用程序”，这使用户能够以直观且方便的方式执行各种计算任务。迅速发展的移动应用程序市场迅速发展的性能硬件和软件平台支持越来越复杂的功能。为了使应用程序在苹果的应用商店或Google Play等市场上取得成功，必须按预期运作，因此必须经过良好的测试。但是，使它们流行的移动应用程序的独特方面，例如基于触摸的接口，快速发展的平台以及传感器等上下文功能，使它们难以有效，有效地测试。此外，随着移动应用程序的市场成熟，开发人员必须确保其应用程序在无数设备上运行良好，同时通过应用商店评论来解决越来越大的用户群的反馈。这些挑战表明，移动开发人员需要实用的自动支持，以确保对其应用进行充分测试。该研究项目旨在设计并彻底验证一种为了克服上述挑战的移动应用程序的自动测试方法。反过来，预计这项研究实现的技术将有助于改善，更高质量的移动应用程序，从而使我们的社会受益，因为我们的社会越来越依赖智能手机应用程序以及创建它们的开发人员和团队。 为了解决这些基本挑战，该项目旨在开发一个自动测试框架，该框架结合了移动应用程序的新型统计表示和通过采矿软件存储库收集的信息，以有效地生成实用，有效的测试场景。更具体地说，将开发一个新颖的测试框架，即T+。 T+植根于基于概率模型的移动应用程序表示。该模型将启用一种变革性的自动化方法，用于生成与低级别事件解耦，可以在不同设备上执行的可行测试用例，并支持多个测试目标和足够的标准。此外，这项研究工作将定义和开发监测机制，以识别基础平台和第三方库中的变更和故障 - 容易发生的API，以及信息丰富的评论。将此信息纳入T+的统计模型将允许生成和优先列表涵盖这些API和评论的测试用例。这项工作的更广泛的影响将存在于（1）改善测试移动应用程序的实践状态，在确保应用程序对更改平台，API，评论和众多设备方面的应用程序进行了充分测试； （2）与行业合作伙伴一起展示改进的测试实践，这将记录为其他开发组织和测试中心的最佳实践； （3）作为该研究项目的一部分，开发教育课程内容并在课堂上进行试验； （4）在本研究计划中积极地涉及代表性不足的学生类别。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，认为值得通过评估来获得支持。

项目成果

Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps

• DOI: 10.1109/tse.2018.2844788
• 发表时间: 2020-02-01
• 期刊: IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
• 影响因子: 7.4
• 作者: Moran, Kevin;Bernal-Cardenas, Carlos;Poshyvanyk, Denys
• 通讯作者: Poshyvanyk, Denys

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

• DOI: 10.1145/3340544
• 发表时间: 2018-12
• 期刊: ACM Transactions on Software Engineering and Methodology (TOSEM)
• 作者: Michele Tufano;Cody Watson;G. Bavota;M. D. Penta;Martin White;D. Poshyvanyk