An efficient parameter estimation of software reliability growth models using gravitational search algorithm

Ankur Choudhary (), Anurag Singh Baghel and Om Prakash Sangwan
Additional contact information
Ankur Choudhary: Amity University Uttar Pradesh
Anurag Singh Baghel: Gautam Buddha University
Om Prakash Sangwan: Guru Jambheshwar University

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 1, No 8, 79-88

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.

Keywords: Gravitational search; Parameter estimation; Software reliability growth model; Metaheuristics (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-016-0541-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-016-0541-0

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-016-0541-0

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().