

> In one of my works on that topic I, indeed, viewed this as a Rasch type
> model. And I well remember how excited I got when reading above paper,
> because that would allow me to deal with predictors in a straight forward
> way.

> However, the number of undetected flaws is crucial by itself as it means to
> go on testing. Capture-recapture models are a good way to estimate these,
> but they don't allow for predictors. So, if I run a crossed mixed effects
> logistic regression on my data, I have missing values.

I am quite ignorant of these matters, but guess that you can run your GLMM on the complete data to 
estimate the "capture probabilities" for each type of fault for each tester, and in a separate step
use these a la Horvitz-Thompson to estimate total number of each fault type conditional on 
your current set of testers.  My first thought was there is a closed population of faults, but I
just had a sudden dizzying image of a hierarchy of more and more subtle problems ;) 




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