The main theme of this workshop will be to foster an open dialogue between the community (i.e., academic and industrial researchers) related to how to best build a community infrastructure to support research on Android testing and analysis.
Just as mobile app developers face domain specific challenges, so do researchers working in this domain, including challenges such as
(1) the rapid evolution of mobile platforms and apps,
(2) the scale of data collection needed, particularly for supporting increasingly popular machine learning techniques, and
(3) the issues related to the reliability and usability of static and dynamic analysis tools.
The research community is in need of a shared infrastructure that can help support and accelerate research related to data collection and automated analysis for mobile apps. As such, the goal of this workshop will be to put together a comprehensive report that outlines the community needs around infrastructure and datasets for supporting research on mobile app testing and analysis.
Topics of the workshop will include, but are not limited to the following:
• Data that researchers need for testing-related and program analysis research for mobile apps.
• Automated tooling and infrastructure for conducting testing-related and program analysis research for mobile apps.
• Best practices for large-scale data collection efforts, and the practices required to maintain these datasets and keep them up to date.
• Infrastructure and tooling for static, dynamic, and security analysis of mobile apps.
• Best practices from practitioners for conducting research related to mobile app testing and analysis.
• Design requirements for the construction of a shared community infrastructure that supports research related to mobile app testing and analysis
Call for Papers
Much of the research related to improving the quality of mobile apps, or automating mobile app development practices makes use of two major categories of artifacts:
(i) mobile application data (both static and dynamic) and
(ii) mobile application analysis techniques & tools.
For instance, a project related to automating mobile testing may aim to mine dynamic interaction traces for mobile apps to train a machine learning model for testing app UIs; conversely, security researchers may aim to use security-focused, static analysis tools that can analyze the complex event-driven nature of Android app code if the researchers wish to study the effectiveness of such tools. Unfortunately, past research on mobile app analysis, testing, and quality has used datasets and tools that have largely been created in an ad-hoc manner, leading to issues related to reproducibility and replicability, therefore hampering potential follow-up work on these topics.
Given the fragmentation around the development of datasets and research tooling related to mobile apps, this workshop aims to bring together researchers and practitioners to elicit design requirements related to building a shared infrastructure, and to provide a forum for sharing best practices for building complete, maintainable and open datasets and tools to support Android testing and analysis research.
We solicit abstracts of up to two pages from researchers who would like to participate. The review and evaluation process will focus on whether the abstracts include the following criteria:
(1) problems associated with mobile testing and analysis research,
(2) data and tooling that are needed to support future research, and
(3) solutions and needs for a community dataset and infrastructure for mobile testing and analysis research.
Abstracts will not be published in the proceedings and need not address all stated criteria, but they must address at least one of the aforementioned criteria. Authors of abstracts that are accepted will be asked to speak and present their thoughts on mobile testing and analysis research. Funding to attend the workshop may be provided to those that submit an abstract.
Attendees need not submit an abstract.