An application of fuzzy clustering to software quality prediction

The ever increasing demand for high software reliability requires more robust modeling techniques for software quality prediction. The paper presents a modeling technique that integrates fuzzy subtractive clustering with module-order modeling for software quality prediction. First fuzzy subtractive clustering is used to predict the number of faults, then module-order modeling is used to predict whether modules are fault-prone or not. Note that multiple linear regression is a special case of fuzzy subtractive clustering. We conducted a case study of a large legacy telecommunication system to predict whether each module will be considered fault-prone. The case study found that using fuzzy subtractive clustering and module-order modeling, one can classify modules which will likely have faults discovered by customers with useful accuracy prior to release.