Issue 42, August 2004

In RGA, what does the Beta (hyp.) value indicate when using the Crow Extended model?

If the Beta (hyp.) value is displayed in the Results Area of the Data Folio Control Panel after you have calculated the parameters using the Crow Extended model, then the bounds on the value of beta do not include one. The confidence level used for this calculation is equal to one minus the significance level, which is specified on the Data Folio page of the User Setup. In addition, if the Beta=1 Hypothesis Invalid option is selected on the Calculations page of the User Setup, then when you calculate the parameters, a message will be displayed indicating that the assumption of beta equal to one may not be valid, as shown next.

You can also verify this by viewing the Beta Bounds plot.

While the Crow-AMSAA and Crow Extended models are applicable within a test phase, how can reliability growth be tracked across test phases in RGA?

To track the reliability growth across test phases, you must utilize another data type and model in RGA. You can convert the demonstrated MTBF at the end of each phase to a reliability value and then use the reliability data type to calculate the parameters. You can then use the exponential assumption to obtain the reliability.

How can the improvements across different stages be evaluated in reliability growth analysis?

This can be accomplished in RGA by calculating the initial MTBF at the beginning of each stage. The initial MTBF is calculated using:

The initial MTBF can be obtained using the Function Wizard in RGA. Once you have obtained the initial MTBF, you can then compare it to the demonstrated MTBF value of the previous stage to evaluate the impact of the improvements between stages. If you are using the Crow Extended model in order to predict the MTBF jump due to delayed fixes, then you can use this method in order to compare the predicted projected MTBF to the initial MTBF of the next stage. This will give you a better understanding of the "true" average effectiveness factor and the assumed one. You can then utilize this information in future projects in order to assign more realistic effectiveness factors when conducting projections.

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