TY - CHAP A1 - Maier, Robert A1 - Mottok, Jürgen ED - Trapp, Mario ED - Saglietti, Francesca ED - Spisländer, Marc ED - Bitsch, Friedemann T1 - BayesianSafety - an Open-Source Package for Causality-Guided, Multi-Model Safety Analysis T2 - Computer Safety, Reliability, and Security, 41st International Conference (SAFECOMP 2022): Munich, Germany, September 6–9, 2022, Proceedings T2 - Lecture notes in computer science ; 13414 N2 - Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool’s architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches. KW - Package BayesianSafety KW - Causality KW - Bayesian Networks KW - Event Tree Analysis KW - Fault Tree Analysis Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-14835-4_2 SP - 17 EP - 30 PB - Springer Nature CY - Cham ER -