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In this research we use a data stream approach to mining data and constructDecision Tree models that predict software build outcomes in terms of softwaremetrics that are derived from source code used in the software constructionprocess. The rationale for using the data stream approach was to track theevolution of the prediction model over time as builds are incrementallyconstructed from previous versions either to remedy errors or to enhancefunctionality. As the volume of data available for mining from the softwarerepository that we used was limited, we synthesized new data instances throughthe application of the SMOTE oversampling algorithm. The results indicate thata small number of the available metrics have significance for predictionsoftware build outcomes. It is observed that classification accuracy steadilyimproves after approximately 900 instances of builds have been fed to theclassifier. At the end of the data streaming process classification accuraciesof 80% were achieved, though some bias arises due to the distribution of dataacross the two classes over time.

Synthetic Minority Over-sampling TEchnique(SMOTE) for Predicting Software Build Outcomes