Context: Software fault prediction has been an important research topic in the software engineering field for more than 30 years. Software defect prediction models are commonly used to detect faulty software modules based on software metrics collected during the software development process. Objective: Data mining techniques and machine learning studies in the fault prediction software context ...

Software quality and reliability have become the main concern during the software development. It is very difficult to develop software without any fault. The fault-proneness of a software module is the probability that the module contains faults and a software fault is a defect that causes software failures in an executable project. Early detection of fault prone software components enables ve...

Object oriented software metrics are computed and used in predicting software quality attributes of object oriented systems. Mapping software metrics to software quality attributes like fault prediction is a complex process and requires extensive computations. Many models have been proposed for fault prediction. Since accuracy is of prime importance in prediction models they are being constantl...

Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault pr...

Prediction of fault-prone modules provides one way to support software quality engineering. Clustering is used to determine the intrinsic grouping in a set of unlabeled data. 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 Obj...