Prediction of software faults using fuzzy nonlinear regression modeling

Software quality models can predict the risk of faults in modules early enough for cost-effective prevention of problems. This paper introduces the fuzzy nonlinear regression (FNR) modeling technique as a method for predicting fault ranges in software modules. FNR modeling differs from classical linear regression in that the output of an FNR model is a fuzzy number. Predicting the exact number of faults in each program module is often not necessary. The FNR model can predict the interval that the number of faults of each module falls into with a certain probability. A case study of a full-scale industrial software system was used to illustrate the usefulness of FNR modeling. This case study included four historical software releases. The first release's data were used to build the FNR model, while the remaining three releases' data were used to evaluate the model. We found that FNR modeling gives useful results.