Software Defect Prediction using Convolutional Neural Network

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Wongpheng:2020:ITC-CSCC,

• author = "Kittisak Wongpheng and Porawat Visutsak",
• booktitle = "2020 35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)",
• title = "Software Defect Prediction using Convolutional Neural Network",
• year = "2020",
• pages = "240--243",
• abstract = "The crucial part in software development lifecycle is finding the software faults. Detecting the faults in an early stage of software lifecycle can prevent the susceptibility and cost overruns. Many machine learning algorithms have been adopted for predicting the error-prone of software system such as Support Vector Machine (SVM), Bayesian Belief Network, Naive Bayes, and Genetic Programming. In this paper, the Convolution Neural Network (CNN) is used to detect the defective modules in software system. This work used the static code metrics for a collection of software modules in five selective NASA datasets. The experimental results show that CNN was promising for defect prediction with an average accuracy of 70.2percent.",
• keywords = "genetic algorithms, genetic programming, Software reliability, Measurement, Software systems, NASA, Convolution, Predictive models, Software fault, Software reliability, Software defect prediction, Convolution Neural Network, ANN, Machine learning, Deep learning Introduction",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=9182919",
• month = jul,
• notes = "Also known as \cite{9182919}",

}

Genetic Programming entries for Kittisak Wongpheng Porawat Visutsak

Citations