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DOE OSTI · 1329800

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

Chu, Tsong-Lun·Varuttamaseni, Athi·Baek, Joo-Seok
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Abstract

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRAs of nuclear power plants (NPPs), and (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware, development of a philosophical basis for defining software failure, and identification of desirable attributes of quantitative software reliability methods. Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. This paper describes a statistical software testing approach for quantifying software reliability and applies it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL).

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21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTSATR REACTORCOMPUTER CODESCOMPUTERIZED SIMULATIONDIGITAL SYSTEMSFAILURESNUCLEAR POWER PLANTSPROBABILISTIC ESTIMATIONREACTOR CONTROL SYSTEMSREACTOR INSTRUMENTATIONREACTOR LICENSINGRELIABILITYRISK ASSESSMENTStatistical Testing ApproachTESTING

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Chu, Tsong-Lun, Varuttamaseni, Athi, Baek, Joo-Seok. 2016-11-01. A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System. https://doi.org/10.2172/1329800

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