Effort estimation of software project

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Merugu:2012:IJARCET,

• title = "Effort estimation of software project",
• author = "R Raja Ramesh Merugu and Venkat Ravi Kumar Dammu",
• journal = "International Journal of Advanced Research in Computer Engineering \& Technology",
• publisher = "Shri Pannalal Research Institute of Technology",
• year = "2012",
• keywords = "genetic algorithms, genetic programming, SBSE, effort estimation, fuzzy logic, particle swarm optimisation, MMRE, neural networks",
• ISSN = "22781323",
• bibsource = "OAI-PMH server at www.doaj.org",
• oai = "oai:doaj-articles:65e54283cfc94cdfd7b789a43a65f1b0",
• URL = "http://ijarcet.org/wp-content/uploads/IJARCET-VOL-1-ISSUE-10-33-41.pdf",
• URL = "http://ijarcet.org/?p=1249",
• abstract = "The effort invested in a software project is probably one of the most important and most analysed variables in recent years in the process of project management. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering in fuzzy logic, artificial neural networks and evolutionary computation. It is important to mention here that these methodologies are complementary and synergistic rather than competitive. They provide in one form or another flexible information processing capability for handling real life ambiguous situations. These methodologies are currently used for reliable and accurate estimate of software development effort which has always been a challenge for both the software industry and academia. The aim of this study is to analyse soft computing techniques in the existing models and to provide in depth review of software and project estimation techniques existing in industry and literature based on the different test datasets along with their strength and weaknesses.",

}

Genetic Programming entries for R Raja Ramesh Merugu Venkat Ravi Kumar Dammu

Citations