ICST 2024
Mon 27 - Fri 31 May 2024 Canada

This program is tentative and subject to change.

Wed 29 May 2024 16:30 - 16:50 at Room 2 - Testing Autonomous Driving Systems

Nearly a decade of research in software engineering has focused on automating mobile app testing to help engineers in overcoming the unique challenges associated with the software platform. Much of this work has come in the form of Automated Input Generation tools (AIG tools) that dynamically explore app screens. However, such tools have repeatedly been demonstrated to achieve lower-than-expected code coverage - particularly on sophisticated proprietary apps. Prior work has illustrated that a primary cause of these coverage deficiencies is related to so-called tarpits, or complex screens that are difficult to navigate.

In this paper, we take a critical step toward enabling AIG tools to effectively navigate tarpits during app exploration through a new form of automated semantic screen understanding. We introduce AURORA, a technique that learns from the visual and textual patterns that exist in mobile app UIs to automatically detect common screen designs and navigate them accordingly. The key idea of AURORA is that there are a finite number of mobile app screen designs, albeit with subtle variations, such that the general patterns of different categories of UI designs can be learned. As such, AURORA employs a multi-modal, neural screen classifier that is able to recognize the most common types of UI screen designs. After recognizing a given screen, it then applies a set of flexible and generalizable heuristics to properly navigate the screen. We evaluated AURORA both on a set of 12 apps with known tarpits from prior work, and on a new set of five of the most popular apps from the Google Play store. Our results indicate that AURORA is able to effectively navigate tarpit screens, outperforming prior approaches that avoid tarpits by 19.6% in terms of method coverage. The improvements can be attributed to AURORA’s UI design classification and heuristic navigation techniques.

This program is tentative and subject to change.

Wed 29 May

Displayed time zone: Eastern Time (US & Canada) change

15:30 - 17:00
Testing Autonomous Driving SystemsResearch Papers / Testing Tools and Demonstration at Room 2
15:30
20m
Research paper
Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving
Research Papers
Andréa Doreste , Matteo Biagiola Università della Svizzera italiana, Paolo Tonella USI Lugano
15:50
20m
Research paper
Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing
Research Papers
Stefano Carlo Lambertenghi Technische Universität München, fortiss GmbH, Andrea Stocco Technical University of Munich & fortiss
Pre-print
16:10
20m
Research paper
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Research Papers
Ruben Grewal , Paolo Tonella USI Lugano, Andrea Stocco Technical University of Munich & fortiss
Pre-print
16:30
20m
Research paper
AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding
Research Papers
Safwat Ali Khan George Mason University, Wenyu Wang University of Illinois Urbana-Champaign, Yiran Ren , Bin Zhu , Jiangfan Shi , Wing Lam George Mason University, Kevin Moran University of Central Florida
Pre-print
16:50
10m
Demonstration
U-Fuzz: A Tool for Stateful Fuzzing of IoT Protocols on COTS Devices
Testing Tools and Demonstration
Shang Zewen , Matheus Eduardo Garbelini , Sudipta Chattopadhyay Singapore University of Technology and Design