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

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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.