PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Edward Chen Co-author(s): Han Bao han.bao@inl.gov
Tate Shorthill tate.shorthill@inl.gov
Carl Elks crelks@vcu.edu
Nam Dinh ntdinh@ncsu.edu
| Application of Orthogonal-Defect Classification for Software Reliability Analysis |
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| Modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate risk methodologies by both the Nuclear Regulatory Commission and industry. This has resulted in hesitation for further modernization projects until a more unified methodology is recognized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) potential failure modes of a DI&C software system. The semantic classification model is used to address the question: how do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: given the connection between latent defects and software failure root causes, how can we quantify the risk (or reliability) of the software? A case study was conducted on a representative software platform (ChibiOS) running a sensor acquisition software developed by Virginia Commonwealth University. The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of existing methods. |
Paper EC304 Preview
Author and Presentation Info
Lead Author Name: Edward Chen (echen2@ncsu.edu)
Bio: Edward is a 4th year Ph.D. candidate researching risk and reliability in digital instrumentation and control systems at North Carolina State University under the direction of Dr. Nam Dinh. His primary areas of focus include risk quantification and model development in conventional PLC based as well as data-driven ML control and information systems. He has worked with multiple groups including Kairos power as a simulation developer for transient cases as well as a contractor for Idaho National Laboratories under the Light Water Sustainability Project. He has also worked on ARPA-e projects such as the Near Autonomous Management and Control system and has developed multiple data-driven autonomous safety systems.
Country: United States of America
Company: North Carolina State University
Job Title: Research Assistant
Bio: Edward is a 4th year Ph.D. candidate researching risk and reliability in digital instrumentation and control systems at North Carolina State University under the direction of Dr. Nam Dinh. His primary areas of focus include risk quantification and model development in conventional PLC based as well as data-driven ML control and information systems. He has worked with multiple groups including Kairos power as a simulation developer for transient cases as well as a contractor for Idaho National Laboratories under the Light Water Sustainability Project. He has also worked on ARPA-e projects such as the Near Autonomous Management and Control system and has developed multiple data-driven autonomous safety systems.
Country: United States of America
Company: North Carolina State University
Job Title: Research Assistant