naa a22 4500 510791204 CHVBK 20180411083320.0 cr unu---uuuuu 180411e20131201xx s 000 0 eng 10.1007/s11334-013-0214-y doi (NATIONALLICENCE)springer-10.1007/s11334-013-0214-y Software health management with Bayesian networks [Elektronische Daten] [Johann Schumann, Timmy Mbaya, Ole Mengshoel, Knot Pipatsrisawat, Ashok Srivastava, Arthur Choi, Adnan Darwiche] Software health management (SWHM) is an emerging field which addresses the critical need to detect, diagnose, predict, and mitigate adverse events due to software faults and failures. These faults could arise for numerous reasons including coding errors, unanticipated faults or failures in hardware, or problematic interactions with the external environment. This paper demonstrates a novel approach to software health management based on a rigorous Bayesian formulation that monitors the behavior of software and operating system, performs probabilistic diagnosis, and provides information about the most likely root causes of a failure or software problem. Translation of the Bayesian network model into an efficient data structure, an arithmetic circuit, makes it possible to perform SWHM on resource-restricted embedded computing platforms as found in aircraft, unmanned aircraft, or satellites. SWHM is especially important for safety critical systems such as aircraft control systems. In this paper, we demonstrate our Bayesian SWHM system on three realistic scenarios from an aircraft control system: (1) aircraft file-system based faults, (2) signal handling faults, and (3) navigation faults due to inertial measurement unit (IMU) failure or compromised Global Positioning System (GPS) integrity. We show that the method successfully detects and diagnoses faults in these scenarios. We also discuss the importance of verification and validation of SWHM systems. Springer-Verlag London (outside the USA), 2013 Software health management nationallicence Fault detection and diagnosis nationallicence Aircraft control system nationallicence Bayesian network nationallicence Probabilistic diagnosis nationallicence Schumann Johann SGT, Inc., NASA Ames Research Center, Moffett Field, CA, USA aut Mbaya Timmy RIACS/USRA, Mountain View, CA, USA aut Mengshoel Ole Carnegie Mellon University, Silicon Valley Campus, Moffett Field, CA, USA aut Pipatsrisawat Knot University of California, Los Angeles, CA, USA aut Srivastava Ashok NASA Ames Research Center, Moffett Field, CA, USA aut Choi Arthur University of California, Los Angeles, CA, USA aut Darwiche Adnan University of California, Los Angeles, CA, USA aut Innovations in Systems and Software Engineering Springer London 9/4(2013-12-01), 271-292 1614-5046 9:4<271 2013 9 11334 https://doi.org/10.1007/s11334-013-0214-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11334-013-0214-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Schumann Johann SGT, Inc., NASA Ames Research Center, Moffett Field, CA, USA aut NATIONALLICENCE 700 1- Mbaya Timmy RIACS/USRA, Mountain View, CA, USA aut NATIONALLICENCE 700 1- Mengshoel Ole Carnegie Mellon University, Silicon Valley Campus, Moffett Field, CA, USA aut NATIONALLICENCE 700 1- Pipatsrisawat Knot University of California, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Srivastava Ashok NASA Ames Research Center, Moffett Field, CA, USA aut NATIONALLICENCE 700 1- Choi Arthur University of California, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Darwiche Adnan University of California, Los Angeles, CA, USA aut NATIONALLICENCE 773 0- Innovations in Systems and Software Engineering Springer London 9/4(2013-12-01), 271-292 1614-5046 9:4<271 2013 9 11334 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer