A K-Means Based Approach for Prediction of Level of Severity of Faults in Software System
Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. Among various clustering techniques available in literature K-means clustering approach is most widely being used. This paper introduces K-means based Clustering approach for software finding the fault proneness of the Object-Oriented systems.