SOFTWARE DEFECT PREDICTION USING ENSEMBLE OF MACHINE LEARNING TECHNIQUE

Abstract

Presently a days research on software defect prediction has pulled in numerous scientists since it helps in production of effective software. Extra bit of leeway is that it helps in decrease of the software advancement cost and encourages strategies to recognize the purposes behind deciding the level of defect-inclined software in future. For explicit kinds of AI, there is no convincing proof that will be more productive and precise in anticipating software defects. A portion of the past related work, in any case, proposes the learning strategies of the ensemble as a more exact other option. This work presents the resample method with three kinds of ensemble students; boosting, stowing, stacking and casting a ballot utilizing four base students on various variants of same dataset storehouse gave in the PROMISE archive. Results show that precision has been improved utilizing ensemble strategies more than single leaners.