Hello, Thank you for your quick and detailed answer. It looks like I haven't given enough details on the problem I am facing. Actually, I work on improving the testing of communications systems, which is mainly software testing. Once the systems are developped, they are tested before being delivered. When running those tests (unit testing, interface testing, field trials, ...), each bug discovered gives birth to a report. This report is then backed to someone who can fix the bug, earlier in the life-cycle of the product.
I'm trying to focus on "at which time bugs are mainly introduced" so that tests can be planned at the right time. We are simply trying here to feed-back our experience, which is not so simple! Software testing has improved a lot since the 90's. I have found a few bug classifications, and one seems particularly attractive to me. It was set by Boris Beizer and has multiple sub-categories. It seems to be commonly admitted as a good classification, and is adapted to my case. I will only use the first nine categories which are : 1. functionnal bugs: requirements and features 2. functionality as implemented 3. structural bugs 4. data (...) 8. test definition and execution bugs 9. others I have more than 3000 bug reports. Understanding them all is as complex as the system is. This is why I want to sample it. Let's say that I will pick one report every 10 reports. This number is not fixed yet. My problem is here. What I have read about the chi square seems quite interesting. I would like to say that the conclusion deducted from my sample is : "I have a 90% probability that the real proportion of structural bug (category 4) is inside the interval 10% to 20%." This interval will be given by my sampling. I hope that my knowledge of english does not make this sentence to foggy (???). Thanks again, Louis Tillier. [EMAIL PROTECTED] . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
