Predicting defects in imbalanced data using resampling methods: an empirical investigation.

ABSTRACT: The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. In addition to this large number of software metrics degrades the model performance. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive comparative analysis of 10 resampling methods to generate effective machine learning models for imbalanced data, (3) inclusion of stable performance evaluators-AUC, GMean, and Balance and (4) integration o

SUBMITTER: Malhotra R 

PROVIDER: S-EPMC9137963 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature