SEAMS 2020
Mon 29 June - Fri 3 July 2020
co-located with ICSE 2020
Mon 29 Jun 2020 14:30 - 15:45 at SEAMS - Opening & Keynote 1 Chair(s): Radu Calinescu

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 29 Jun

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14:00 - 15:45
Opening & Keynote 1SEAMS 2020 at SEAMS
Chair(s): Radu Calinescu University of York, UK
Day opening
SEAMS Opening
SEAMS 2020
Shinichi Honiden Waseda University / National Institute of Informatics, Japan, Radu Calinescu University of York, UK, Elisabetta Di Nitto Politecnico di Milano
On the Probabilistic Analysis of Neural NetworksKeynote
SEAMS 2020
Corina S. Păsăreanu Carnegie Mellon University Silicon Valley, NASA Ames Research Center
Media Attached