A novel software defect prediction approach using modified objective cluster analysis

2020 ◽  
Author(s):  
Junhua Ren ◽  
Qing Zhang
Keyword(s):  
Cluster Analysis ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Prediction Approach ◽  
Objective Cluster

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

scholarly journals A cluster-analysis-based feature-selection method for software defect prediction

2016 ◽  
Vol 46 (9) ◽  
pp. 1298-1320 ◽  
Author(s):  
Qing GU ◽  
Shulong LIU ◽  
Wangshu LIU ◽  
Daoxu CHEN ◽  
Xiang CHEN
Keyword(s):  
Cluster Analysis ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

Research of Software Defect Prediction Model Based on ACO-SVM

2011 ◽  
Vol 34 (6) ◽  
pp. 1148-1154 ◽  
Author(s):  
Hui-Yan JIANG ◽  
Mao ZONG ◽  
Xiang-Ying LIU
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Model Based ◽  
Software Defect

Software Defect Prediction via Deep Belief Network

2019 ◽  
Vol 28 (5) ◽  
pp. 925-932
Author(s):  
Hua WEI ◽  
Chun SHAN ◽  
Changzhen HU ◽  
Yu ZHANG ◽  
Xiao YU
Keyword(s):  
Deep Belief Network ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Belief Network ◽  
Software Defect
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