Manuscript Title:

PREDICTION OF SOFTWARE DEFECTS USING ENSEMBLE LEARNING: REVIEW

Author:

TARIQ N. AL-HADIDI, SAFWAN O. HASOON

DOI Number:

DOI:10.17605/OSF.IO/Q59B7

Published : 2023-05-10

About the author(s)

1. TARIQ N. AL-HADIDI - Software Engineering Department, Collage of Computer Science and Mathematics, University of Mosul, Mosul, Iraq.
2. SAFWAN O. HASOON - Software Engineering Department, Collage of Computer Science and Mathematics, University of Mosul, Mosul, Iraq.

Full Text : PDF

Abstract

Compilation calculations are now frequently used in various software quality assurance processes. These classifiers showed a better implementation of their component models. Despite the fact that ensemble approaches are regularly used in urgent areas such as Programming Distortion Predictions (SDP) and Programming Change Predictions (SCP), the most recent exploration into their application requires careful evaluation. The objective of the review is to assess, scan, and mark any neglected exploration needs associated with the use of Group Policy in SDP and SCP. This study includes a comprehensive evaluation of the research in light of the classification, application, and rules of definition, implementation, and potential threats to the progress that was used. Important criteria used to rank grouped strategies are similarity, grouping, connectivity, diversity, and reliance on key ideal models. It's also proven useful for a wide number of uses, including learning computation for making SDP/SCP models and taking care of class imbalance. The survey findings arguably support the need for further examination to propose, evaluate, approve and look at different classes of diverse innovations for a variety of SDP/SCP applications, including web-based education.


Keywords

Ensemble learning, Software defect prediction, Software quality, Machine Learning.