@inproceedings{348cda9ebfb0452a9c5dddcb3f3cfdd0,
title = "An Approach to Modeling Postulated Software Common Cause Failures of Diverse Digital Instrumentation and Control Systems",
abstract = "This work presents an approach to modeling potential software common cause failures (CCFs) within diverse digital instrumentation and control (DI&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. Defenses against CCF often rely on the concept of diversity which is a method used to reduce the commonality of components and lower the probability of postulated CCFs. However, the influence of diversity on software-based CCFs remains a topic of research. Software failures are caused by the activation of defects within software, the existence of which can be due to human mistakes during software development activities. It is hypothesized that diverse software may share defects due to common human errors made during their respective development activities. This results in a set of common defects that can lead to common failure of otherwise diverse software. This work proposes a modeling approach to directly identify the commonality of diverse software. The Bayesian and Human Reliability Analysis (HRA)-Aided Method for the Reliability Analysis of Software (BAHAMAS) was previously developed to assess software reliability by tracing defects to human errors within the software development life cycle. This work demonstrates an application of software BAHAMAS for assessing CCFs of diverse software configurations by considering their commonality of development. The new approach can support design decisions for implementing software diversity within DI&C systems.",
keywords = "Digital I&C, Diversity, Software CCF",
author = "Tate Shorthill and Han Bao and Edward Chen and Sai Zhang and Heng Ban",
note = "Funding Information: The research activities and achievements documented in this paper were funded by the U.S. DOE{\textquoteright}s Light Water Reactor Sustainability Program, Risk Informed Systems Analysis Pathway. This submitted manuscript was authored by a contractor of the U.S. Government under DOE Contract No. DE-AC07-05ID14517. Accordingly, the U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. This information was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof. Publisher Copyright: {\textcopyright} 2023 American Nuclear Society, Incorporated.; 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023 ; Conference date: 15-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.13182/NPICHMIT23-41074",
language = "English",
series = "Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023",
publisher = "American Nuclear Society",
pages = "1100--1109",
booktitle = "Proceedings of 13th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC and HMIT 2023",
}