That's a really interesting question (and one for which I don't know enough probability theory to give a good answer). At the very least, if you have N testers

That's a really interesting question (and one for which I don't know enough probability theory to give a good answer). At the very least, if you have N testers you can apply the technique pairwise to generate N(N - 1)/2 estimates for the number of bugs. Taking the average or median of these estimates would probably give you a more robust value for the total number of bugs. You could also build a correlation matrix between testers (i.e. are bugs really independently found or do some testers tend to find the same bugs) and you could test the assumption that bugs are equally hard to find (which realistically isn't the case). With that new insight into your original data, you could compute an a posteriori estimate for the total number of bugs, Bayes style. (In other words, you start with the prior assumption that testers are independent and bugs are equally hard to find to build a first answer. Based on this estimate, you revise your assumption with a more realistic probability distribution for bugs and testers, and use that distribution to compute a new answer) Or, maybe just stop messing with R and get on with fixing all the bugs found by your N testers.