A Model for Early Prediction of Faults in Software Systems

Quality of a software component can be measured in terms of fault proneness of data. Quality estimations are made using fault proneness data available from previously developed similar type of projects and the training data consisting of software measurements. To predict faulty modules in software data different techniques have been proposed which includes statistical method, machine learning methods, neural network techniques and clustering techniques. Predicting faults early in the software life cycle can be used to improve software process control and achieve high software reliability. The aim of proposed approach is to investigate that whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using decision tree based Model in combination of K-means clustering as preprocessing technique. This approach has been tested with CM1 real time defect datasets of NASA software projects. The high accuracy of testing results show that the proposed Model can be used for the prediction of the fault proneness of software modules early in the software life cycle.