We're Finding Most of the Bugs, but What are We Missing?

We compare two types of model that have been used to predict software fault-proneness in the next release of a software system. Classification models make a binary prediction that a software entity such as a file or module is likely to be either faulty or not faulty in the next release. Ranking models order the entities according to their predicted number of faults. They are generally used to establish a priority for more intensive testing of the entities that occur early in the ranking. We investigate ways of assessing both classification models and ranking models, and the extent to which metrics appropriate for one type of model are also appropriate for the other. Previous work has shown that ranking models are capable of identifying relatively small sets of files that contain 75-95% of the faults detected in the next release of large legacy systems. In our studies of the rankings produced by these models, the faults not contained in the predicted most fault prone files are nearly always distributed across many of the remaining files; i.e., a single file that is in the lower portion of the ranking virtually never contains a large number of faults.