Created by W.Langdon from gp-bibliography.bib Revision:1.8081
Existing work related to MOM(s) use a quantitative prediction model to obtain the predicted rankings of program modules, implying that only the fault prediction error measures such as the average, relative, or mean square errors are minimized. Such an approach does not provide a direct insight into the performance behavior of a MOM. For a given percentage of modules enhanced, the performance of a MOM is gauged by how many faults are accounted for by the predicted ranking as compared with the perfect ranking. We propose an approach for calibrating a multi-objective MOM using genetic programming. Other estimation techniques, e.g., multiple linear regression and neural networks cannot achieve multi objective optimization for MOM(s). The proposed methodology facilitates the simultaneous optimization of multiple performance objectives for a MOM. Case studies of two industrial software systems are presented, the empirical results of which demonstrate a new promise for goal-oriented software quality modeling.",
Genetic Programming entries for Taghi M Khoshgoftaar Yi Liu Jim Seliya