We evaluate inductive logic programming (ILP) methods for predicting fault density in C++ classes. In this problem, each training example is a C++ class deenition, represented as a calling tree, and labeled as \pos-itive" ii faults (i.e., errors) were discovered in its implementation. We compare two ILP systems, FOIL and FLIPPER, and explore the reasons for their diiering performance, using bot...