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Mon 28 Apr 2025 12:00 - 12:15 at 104 - Keynote 2 and Paper Presentations 1 Chair(s): Vincenzo Riccio

Deep learning-based object detection models are widely used for user interface (UI) component identification. However, these models often make errors when they encounter UI configurations different from their training data. Existing methods generate test cases seeding from original test dataset and using manually defined metamorphic relations (MR). To eliminate the dependency on original test data and manually defined MRs, this study proposes DeepUIFuzz, a guided fuzzing methodology that leverages Google’s Material Design layouts as seed templates. Our approach systematically explores UI design space by fuzzing four key style dimensions: color, elevation, fonts, and shape, while maintaining HTML diversity through varied icons and images. All generated layouts were validated as structurally consistent through HTML and CSS validation checks. Additionally, human evaluators assessed the realism of the generated layouts, confirming their usability. Evaluation of the generated test cases against four prominent object detection models (YOLOv3, YOLOv5, SSD, FCOS) demonstrates high Error Finding Rates ranging from 0.86 to 0.95.

Mon 28 Apr

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

11:00 - 12:30
Keynote 2 and Paper Presentations 1SBFT at 104
Chair(s): Vincenzo Riccio University of Udine
11:00
60m
Keynote
Keynote by Marcel Böhme
SBFT
Marcel Böhme MPI for Security and Privacy
12:00
15m
Research paper
DeepUIFuzz: A Guided Fuzzing Strategy for Testing UI Component Detection Models
SBFT
Proma Chowdhury University of Dhaka, Kazi Sakib Institute of Information Technology, University of Dhaka
12:15
15m
Research paper
On Evaluating Fuzzers with Context-Sensitive Fuzzed Inputs: A Case Study on PKCS#1-v1.5
SBFT
S Mahmudul Hasan Syracuse University, Polina Kozyreva Syracuse University, Endadul Hoque Syracuse University
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