Paper Info
Title
Software Reliability Prediction Using Cuckoo Search Optimization, Empirical Mode Decomposition, and ARIMA Model: CS-EEMD-ARIMA Based SRGM
Abstract
AbstractThe demand of highly reliable and superior quality open source software's are increasing day by day. This demand force software developers to improvise the reliability of their software's. Authors have proposed several parametric and non-parametric software reliability models but they have their own limitations, like parametric model suffer from unrealistic model assumptions, operating environment condition dependencies. In contrast to parametric, non-parametric models overcome these issues but they are computationally costlier. So, the scope of optimization or development of new reliable model still exists. This paper presents an effective software reliability modeling based on Cuckoo Search optimization, Ensemble Empirical Mode Decomposition and ARIMA modeling of time series to provide more accurate prediction. Extensive experiments on 5 real datasets is conducted and results are collected. The analysis of results indicates the superiority of proposed technique over existing parametric and non-parametric models for open source software's and propriety software's.
Year
DOI
Venue
2016
10.4018/IJOSSP.2016100103
Periodicals
Field
DocType
Volume
Cuckoo search,Autoregressive integrated moving average,Artificial intelligence,Engineering,Software quality,Machine learning,Hilbert–Huang transform
Journal
7
Issue
ISSN
Citations 
4
1942-3926
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ankur Choudhary132.43
Anurag Singh Baghel241.77