Open AccessThe cross-project defect prediction based on PSO and Feature Dependent Naive Bayes
Author(s) -
Zhexi Yao,
Li Sun,
Tao Zou,
Jinbo Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/2/022126
Subject(s) - naive bayes classifier , particle swarm optimization , computer science , software , feature (linguistics) , machine learning , software bug , artificial intelligence , data mining , classifier (uml) , cross validation , programming language , support vector machine , linguistics , philosophy
The software defect prediction often assumes that the software under test has rich historical information, which is a harsh condition in reality. Cross-project defect prediction can predict projects with rare historical information by using historically informative projects. The differences in software architecture and code environment pose challenges to cross-project software defect prediction. Based on the characteristics of software defect prediction sets, this paper introduces a two-stage algorithm based on particle swarm optimization algorithm and Feature Dependent Naive Bayesian classifier.