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that the new model eliminates the possibility of failure. Properly executed, FMEA
can assist in improving overall satisfaction and safety levels. There are many ways
to evaluate the safety and quality of software products and developmental processes,
but when trying to design safe entities, a proactive approach is far preferable to a
reactive approach.
Probability distribution: Having one prototype that works under controlled condi-
tions does not prove that the design will perform well under other conditions or over
time. Instead a statistical analysis is used to assess the performance of the software
design across the complete range of variation. From this analysis, an estimate of the
probability of the design performing acceptably can be determined. There are two
ways in which this analysis can be performed: 1) Build many samples and test and
measure their performance, or 2) predict the design's performance mathematically.
We can predict the probability of the design meeting the requirement given sources
of variation experienced by a software product. If this probability is not sufficiently
large, then the team can determine the maximum allowable variation on the model's
inputs to achieve the desired output probability. And if the input variation cannot be
controlled, the team can explore new input parameter values that may improve their
design's statistical performance with respect to multiple requirements simultaneously.
The control chart, also known as the Stewart chart or process-behavior chart, in
statistical process control is a tool used to determine whether a process is in a state
of statistical control. If the chart indicates that the process is currently under control,
then it can be used with confidence to predict the future performance of the process.
If the chart indicates that the process being monitored is not in control, the pattern
it reveals can help determine the source of variation to be eliminated to bring the
process back into control. A control chart is a specific kind of run chart that allows
significant change to be differentiated from the natural variability of the process.
This is the key to effective process control and improvement. On a practical level,
the control chart can be considered part of an objective disciplined approach that
facilitates the decision as to whether process (e.g., a Chapter 2 software development
process) performance warrants attention.
We ultimately can expect the technique to penetrate the software industry. Al-
though a few pioneers have attempted to use statistical process control in software-
engineering applications, the opinion of many academics and practitioners is that
it simply does not fit in the software world. These objections probably stem from
unfamiliarity with the technique and how to use it to best advantage. Many tend to
dismiss it simply on the grounds that software can not be measured, but properly
applied, statistical process control can flag potential process problems, even though
it cannot supply absolute scores or goodness ratings.
8.7.3
Sample Optimize Phase DFSS Tools
Axiomatic design implementation in software DFSS is a systematic process, architec-
ture generator, and disciplined problem-prevention approach to achieve excellence.
Robust design is the heart of the software DFSS optimize phase. To ensure the success
of robust parameter design, one should start with good design concepts. Axiomatic
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