Creating UI tests for mobile applications is a difficult and time-consuming task. As such, there has been a considerable amount of work carried out to automate the generation of mobile tests---largely focused upon the goals of maximizing code coverage or finding crashes. However, comparatively fewer automated techniques have been proposed to generate a highly sought after type of test: usage-based tests. These tests exercise targeted app functionalities for activities such as regression testing. In this paper, we present the Avgust tool for automating the construction of usage-based tests for mobile apps. Avgust learns usage patterns from videos of app executions collected by beta testers or crowd-workers, translates these into an app-agnostic state-machine encoding, and then uses this encoding to generate new test cases for an unseen target app. We evaluated Avgust on 374 videos of use cases from 18 popular apps and found that it can successfully exercise the desired usage in 69% of the tests. Avgust is an open-source tool available at https://github.com/felicitia/UsageTesting-Repo/tree/demo/. A video illustrating the capabilities of Avgust can be found at: https://youtu.be/LPICxVd0YAg.
@inproceedings{Talebipour23icse-demo,
author = {Saghar Talebipour and Hyojae Park and Kesina Baral and Leon Yee and
Safwat Ali Khan and Kevin Moran and Yuriy Brun and Nenad Medvidovic and
Yixue Zhao},
title =
{\href{http://people.cs.umass.edu/brun/pubs/pubs/Talebipour23icse-demo.pdf}{{AVGUST}:
{A} Tool for Generating Usage-Based Tests from Videos of App Executions}},
booktitle = {Proceedings of the Demonstrations Track at the 45th International Conference on Software Engineering (ICSE)},
venue = {ICSE Demo},
address = {Melbourne, Australia},
month = {May},
date = {14--20},
pages = {83--87},
year = {2023},
accept = {$\frac{38}{80} \approx 48\%$},
note = {\href{https://doi.org/10.1109/ICSE-Companion58688.2023.00030}{DOI: 10.1109/ICSE-Companion58688.2023.00030}},
doi = {10.1109/ICSE-Companion58688.2023.00030},
abstract = {<p>Creating UI tests for mobile applications is a difficult and time-consuming
task. As such, there has been a considerable amount of work carried out to
automate the generation of mobile tests---largely focused upon the goals of
maximizing code coverage or finding crashes. However, comparatively fewer
automated techniques have been proposed to generate a highly sought after
type of test: usage-based tests. These tests exercise targeted app
functionalities for activities such as regression testing. In this paper, we
present the Avgust tool for automating the construction of usage-based tests
for mobile apps. Avgust learns usage patterns from videos of app executions
collected by beta testers or crowd-workers, translates these into an
app-agnostic state-machine encoding, and then uses this encoding to generate
new test cases for an unseen target app. We evaluated Avgust on 374 videos of
use cases from 18 popular apps and found that it can successfully exercise
the desired usage in 69% of the tests. Avgust is an open-source tool
available at https://github.com/felicitia/UsageTesting-Repo/tree/demo/. A
video illustrating the capabilities of Avgust can be found at:
https://youtu.be/LPICxVd0YAg.</p>},
fundedBy = {NSF CCF-1763423, NSF CCF-1955853, NSF CCF-2210243,
NSF CCF-2106871, NSF CCF-2030859 (CIFellows)},
}