  The 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 reliability
methodologies by both the United States (U.S.) Nuclear Regulatory Commission
(NRC) and the industry. 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 quality assurance that are
verified by experimental evidence. In essence, the approach combines a semantic
failure classification model with a reliability growth model to predict the
probability of failure modes of a software system. A case study was conducted
on a representative I&C platform (ChibiOS) running a smart sensor acquisition
software developed by Virginia Commonwealth University (VCU). The testing and
evidence collection guidance in ORCAS was applied, and defects were uncovered
in the software. Qualitative evidence, such as modified condition decision
coverage, was used to gauge the completeness and trustworthiness of the
assessment while quantitative evidence was used to determine the 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
if the software is reliable (or unreliable) while still leveraging the strength
of the existing methods.
