Improving Code Churn Predictions During the System Test and Maintenance Phases

@inproceedings{Khoshgoftaar1994ImprovingCC,
  title={Improving Code Churn Predictions During the System Test and Maintenance Phases},
  author={Taghi M. Khoshgoftaar and Robert M. Szabo},
  booktitle={ICSM},
  year={1994}
}
In this study, we will show how to improve the prediction of gross change using neural networks. We select a multiple regression quality model from the principal components of software complexity metn’cs collected from a large commercial software system at the beginning of the testing phase. Our measure of quality is based on gross change, and is collected at the end of the maintenance phase. This quality measure is attractive for study as it is both objective and easily obtained directly from… CONTINUE READING
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