Comparing negative binomial and recursive partitioning models for fault prediction
Elaine J. Weyuker, Thomas J. Ostrand, Robert M. Bell · 2008
Two different software fault prediction models have been used to predict the N% of the files of a large software system that are likely to contain the largest numbers of faults. We used the same predictor variables in a negative binomial regression model and a recursive partitioning model, and compared their effectiveness on three large industrial software systems. The negative binomial model identified files that contain 76 to 93 percent of the faults, and recursive partitioning identified files that contain 68 to 85 percent.