Comparison of M5’ Model Tree with MLR in the Development of Fault Prediction Models Involving Interaction Between Metrics

Amongst the critical actions needed to be undertaken before system testing, software fault prediction is imperative. Prediction models are used to identify fault-prone classes and contribute considerably to reduce the testing time, project risks, and resource and infrastructure costs. In the development of a prediction model, the interaction of metrics results in an improved predictive capability, accruing to the fact that metrics are often correlated and do not have a strict additive effect in a regression model.

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