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Sat 3 May 2025 09:10 - 10:30 at 213 - Opening and Keynote Session Chair(s): Jinhan Kim

Software testing is about detecting failures, but not every failure necessarily indicates a genuine fault in the system under test. Some failures are spurious – caused by invalid test inputs or by flaky test outputs. While these spurious failures can arise in many contexts, they are especially prevalent in deep learning–enabled and cyber-physical systems, which often operate autonomously in complex, unpredictable environments. In such systems, virtually any environmental factor can act as an input, and the system’s internal decision-making – driven by deep learning models – may be nondeterministic or difficult to interpret. In this talk, I will discuss three solutions to ensure the validity of test inputs and increase the robustness of test outputs: (1) Generating interpretable constraints that characterize valid test inputs, (2) Developing effort-optimized, human-assisted methods for validating test inputs, and (3) Employing generative models to improve the consistency of test results across different simulation environments. I will illustrate these solutions through case studies from cyber-physical systems, autonomous driving, and network systems.

Sat 3 May

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

09:00 - 10:30
Opening and Keynote SessionDeepTest at 213
Chair(s): Jinhan Kim Università della Svizzera italiana (USI)
09:00
10m
Day opening
Opening
DeepTest

09:10
80m
Keynote
Failures or False Alarms? Validating Tests and Failures for Cyber Physical Systems
DeepTest
Shiva Nejati University of Ottawa
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