A novel modified undersampling (MUS) technique for software defect prediction

P. Lingden, Abeer Alsadoon, P. W.C. Prasad, Omar Hisham Alsadoon, Rasha S. Ali, Vinh Tran Quoc Nguyen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real-world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time. Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1-score to 0.52 ∼ 0.96, and hence it is proximity reached best F1-score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.
Original languageEnglish
Pages (from-to)1003-1020
Number of pages18
JournalComputational Intelligence
Volume35
Issue number4
Early online date18 Jul 2019
DOIs
Publication statusPublished - Nov 2019

Fingerprint Dive into the research topics of 'A novel modified undersampling (MUS) technique for software defect prediction'. Together they form a unique fingerprint.

Cite this