A Study on the Software Reliability Model Using Machine Learning and Deep Learning Method  
Author Youn Su Kim

 
Co-Author(s) Kwang Yoon Song; Hoang Pham; In Hong Chang

 
Abstract Software has become very important in all fields. If the software that performs these various functions breaks down, it will have a huge shock socially and economically. To improve this, the software reliability field for evaluating software reliability appeared, and a software reliability model that can quantitatively evaluate it appeared. However, with the addition of several assumptions, the software reliability model is more likely to fit special cases than general cases. In this study, to overcome problems, a model that relies on given data was proposed, not a model that depends on assumptions through machine learning methods. In this study, we propose a software reliability model based on data rather than a software reliability model based on assumptions by using machine learning method. As a result, the software reliability model using RNN showed good results in 4 out of 5 criteria.

 
Keywords Software Reliability, Machine Learning, Random Forest, Support Vector Machine, Neural Network
   
    Article #:  DSBFI23-57
 
Proceedings of 2nd ISSAT International Conference on Data Science in Business, Finance and Industry
January 8-10, 2023 - Da Nang, Vietnam