With-in-project defect prediction using bootstrap aggregation based diverse ensemble learning technique

Predicting the defect-proneness of a module can reduce the time, effort, manpower, and consequently the cost to develop a software project. Since the causes of software defects are difficult to identify, a wide range of machine learning models are still being developed to build a high performing prediction sys-tems. For this reason, an hybrid approach called - diverse ensemble learning technique (DELT), that adopts two diversity generation schemes such as bootstrap aggregation and multi-inducer concepts, is proposed for with-in-project defect prediction (WPDP) problem in order to mitigate the low classification rates of the prediction model. To predict the final class-label for any unlabeled test module, the proposed DELT employs the principle of majority voting. An extensive set of experiments are conducted on 43 pub-licly available PROMISE and NASA datasets. The experimental results are promising since it improves the generalization performance in classifying the defect proneness of the software module.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).