no code implementations • 21 May 2024 • Sepehr Sharifi, Andrea Stocco, Lionel C. Briand
Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement (safety metric).
no code implementations • 31 Jan 2023 • Sepehr Sharifi, Donghwan Shin, Lionel C. Briand, Nathan Aschbacher
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis.
