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ICSE 2020
Wed 24 June - Thu 16 July 2020

Neural networks are powerful tools for automated decision-making, seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale, reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network. We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

Mon 13 Jul
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15:00 - 17:00: FormaliSE 2020 - Session 2: Keynote and Q+A of paper presentations at Goguryeo
Formalise-2020-papers15:00 - 16:00
Corina S. PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center
Formalise-2020-papers16:00 - 17:00
Live Q&A