Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm

Abstract

In software engineering fault proneness prediction is one of the important fields for quality measurement using multiple code metrics. The metrics thresholds are very practical in measuring the code quality for fault proneness prediction. It helps to improvise the software quality in short time with very low cost. Many researchers are in the race to develop a measuring attribute for the software quality using various methodologies. Currently so many fault proneness prediction models are available. Among that most of the methods are used to identify the faults either by data history or by special supervising algorithms. In most of the real time cases the fault data bases may not be available so that the process becomes tedious. This article proposes a hybrid model for identifying the faults in the software models and also we proposed coupling model along with the algorithm so that the metrics are used to identify the faults and the coupling model couples the metrics and the faults for the developed system software.