Paper Info
Title
An efficient parameter estimation of software reliability growth models using gravitational search algorithm
Abstract
Abstract This paper presents an effective parameter estimation approach for software reliability growth models using gravitational search algorithm. A software reliability growth model is imperfect, if model parameters are unknown and are not validated on real-time software datasets. There exist several efficient numerical estimation techniques for parameter estimation of software reliability growth models. But they are not panacea. Sample size, biasing and initialization etc. always remain a constraint for best parameter estimation. Results indicate that gravitational search algorithm based technique for parameter estimation overcomes these problems and does superior quality parameter estimation. In this paper, extensive experiments on nine real-time datasets were conducted and results were analyzed to compare the proposed approach. The analysis results point towards the superiority of proposed approach over existing numerical estimation, genetic algorithm and cuckoo search methods.
Year
DOI
Venue
2017
10.1007/s13198-016-0541-0
International Journal of Systems Assurance Engineering and Management
Keywords
DocType
Volume
Gravitational search, Parameter estimation, Software reliability growth model, Metaheuristics
Journal
8
Issue
ISSN
Citations 
1
0976-4348
2
PageRank 
References 
Authors
0.39
17
3
Name
Order
Citations
PageRank
Ankur Choudhary132.43
Anurag Singh Baghel241.77
Om Prakash Sangwan3584.69