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Predicting defect-prone software components are an economically important activity and so has received a good deal of attention. The main objective of this software defect-proneness is to propose and evaluate a general framework for defect prediction in software that supports unbiased and comprehensive comparisons between competing prediction systems. Generally, before building defect prediction model and using them for prediction purposes, first it is necessary to decide which learning schemes should be used to construct the model. Thus the predictive performances of the learning scheme(s) should be determined, especially for future data. However, this step is often neglected and so the resultant prediction model may not be trustworthy.
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