Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Software defect prediction using Bayesian networks
Empirical Software Engineering
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
Automated Software Engineering
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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. 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 by using clustering techniques. This approach has been tested with three real time defect datasets of NASA software projects, JM1, PC1 and CM1. Predicting faults early in the software life cycle can be used to improve software process control and achieve high software reliability. The results show that when all the prediction techniques are evaluated, the best prediction model is found to be the fusion of requirement and code metric model.