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.

移动设备已经成为现代社会不可或缺、无处不在的一部分。智能手机和平板电脑的流行很大程度上归功于移动软件的成功，这些软件通常被称为“应用程序”，使用户能够以直观和方便的方式执行各种计算任务。快速发展的高性能硬件和软件平台支持越来越复杂的功能，推动了手机应用市场的蓬勃发展。为了让应用在苹果App Store或b谷歌Play等市场取得成功，它们必须按照预期运行，因此必须经过良好的测试。然而，手机应用的独特之处（如触屏界面、快速发展的平台和传感器等相关功能）使得它们难以进行有效和高效的测试。此外，随着手机应用市场的成熟，开发者必须确保他们的应用能够在各种设备上运行良好，同时通过应用商店的评论处理来自日益庞大的用户基础的反馈。这些挑战表明，移动开发者需要实际的自动化支持，以确保他们的应用得到充分的测试。本研究项目旨在设计并彻底验证移动应用程序的自动化测试方法，以克服上述挑战。反过来，预计这项研究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和开发人员和团队都受益。为了解决这些基本挑战，该项目旨在开发一个自动化测试框架，该框架结合了移动应用程序的新颖统计表示和通过挖掘软件存储库技术收集的信息，以有效地生成实用、有效的测试场景。更具体地说，将开发一个新的测试框架，称为T+。T+根植于基于概率模型的移动应用表示。该模型将启用一种转换的自动化方法，用于生成可行的测试用例，这些测试用例与低级事件解耦，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控机制，以识别底层平台和第三方库中容易发生变化和故障的api，以及信息审查。将这些信息合并到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对其进行优先级排序。这项工作的更广泛的影响将存在于(1)改善移动应用测试实践的状态，在确保应用程序在不断变化的平台、api、审查和众多设备方面得到充分测试方面面临困难；(2)与行业合作伙伴一起演示改进的测试实践，这些实践将被记录为其他开发组织和测试中心采用的最佳实践；(3)作为本研究项目的一部分，开发教育课程内容并在课堂上进行试点；(4)积极让未被充分代表的学生参与到这个研究项目中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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