Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Software Engineering

arXiv:2206.11321 (cs)
[Submitted on 22 Jun 2022]

Title:An Application of a Modified Beta Factor Method for the Analysis of Software Common Cause Failures

Authors:Tate Shorthill, Han Bao, Edward Chen, Heng Ban
View PDF
Abstract:This paper presents an approach for modeling software common cause failures (CCFs) within digital instrumentation and control (I&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. This work emphasizes the importance of identifying software-centric attributes related to the coupling mechanisms necessary for simultaneous failures of redundant software components. The groups of components that share coupling mechanisms are called common cause component groups (CCCGs). Most CCF models rely on operational data as the basis for establishing CCCG parameters and predicting CCFs. This work is motivated by two primary concerns: (1) a lack of operational and CCF data for estimating software CCF model parameters; and (2) the need to model single components as part of multiple CCCGs simultaneously. A hybrid approach was developed to account for these concerns by leveraging existing techniques: a modified beta factor model allows single components to be placed within multiple CCCGs, while a second technique provides software-specific model parameters for each CCCG. This hybrid approach provides a means to overcome the limitations of conventional methods while offering support for design decisions under the limited data scenario.
Comments: 12 pages, 3 Figures, 7 Tables, presented at the Probabilistic Safety Assessment & Management conference in 2022. arXiv admin note: text overlap with arXiv:2204.03717
Subjects: Software Engineering (cs.SE); Systems and Control (eess.SY)
Cite as: arXiv:2206.11321 [cs.SE]
  (or arXiv:2206.11321v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2206.11321

Submission history

From: Tate Shorthill [view email]
[v1] Wed, 22 Jun 2022 18:53:02 UTC (716 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
license icon view license
Current browse context:
cs.SE
< prev   |   next >
new | recent | 2022-06
Change to browse by:
cs
cs.SY
eess
eess.SY

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)