Abstract
This paper presents a method, called SICA (SImple
Complexity Analysis), for the complexity analysis of
application software in computer based reactor protection
systems of nuclear power plants. The complexity measures
are utilised in the estimation of software failure
probabilities. Complexity of software can be defined in
several ways. The challenge is to find a practical and
justifiable metric, which can be assumed to correlate
with the reliability. The goal has been to develop a
simple complexity analysis method, because reactor
protection systems contain typically very many software
modules, and their analysis can be time-consuming. The
complexity analysis is performed based on functional
diagrams used for requirements specification. Software
modules are divided into three complexity categories:
low, medium and high. In SICA, categorisation of modules
is performed based on the number of feedback loops, the
number of connected complex function blocks, the number
of connected function blocks, and the number of inputs
and outputs. The complexity analysis is demonstrated with
application software module examples. The decision rules
of the SICA method are simple to apply and the complexity
category of a software module can be determined by a
visual assessment.
Original language | English |
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Title of host publication | Proceedings of PSAM 13 |
Publisher | International Association of Probabilistic Safety Assessment and Management IAPSAM |
Publication status | Published - 2016 |
MoE publication type | A4 Article in a conference publication |
Event | 13th International Conference on Probabilistic Safety Assessment and Management - Sheraton Grande Walkerhill, Seoul, Korea, Republic of Duration: 2 Oct 2016 → 7 Oct 2016 Conference number: 13 |
Conference
Conference | 13th International Conference on Probabilistic Safety Assessment and Management |
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Abbreviated title | PSAM 13 |
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 2/10/16 → 7/10/16 |
Keywords
- software reliability
- software complexity
- probabilistic risk analysis