Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality

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An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, the authors predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. They apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software.