Open AccessSoftware defect prediction based on CS-BP neural network
Author(s) -
Jingyang Ma
Publication year - 2021
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 - 10.1088/1742-6596/2010/1/012098
Subject(s) - artificial neural network , cuckoo search , computer science , c4.5 algorithm , software , machine learning , artificial intelligence , software bug , field (mathematics) , data mining , support vector machine , particle swarm optimization , naive bayes classifier , operating system , mathematics , pure mathematics
Software defect prediction can detect whether there are defects in the program module so as to effectively reduce the unnecessary cost of software development and maintenance. In this paper, the limitation of the traditional BP neural network in the field of defect prediction leads to the inaccuracy of the prediction results. By using the global optimization ability of cuckoo search, the BP neural network is improved, the important initial parameters of the network are optimized, and the software defect prediction method of CS-BP is proposed. The experimental results show that compared with traditional machine learning algorithms such as BP neural network, J48 and SVM, CS-BP method has a better effect on the prediction of software defects.