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Author(s): R Raja Ramesh Merugu, and Venkat Ravi Kumar Dammu

Journal: International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
ISSN 2278-1323

Volume: 1;
Issue: 10;
Start page: 033;
Date: 2012;
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Keywords: Effort Estimation | Fuzzy Logic | Genetic Programming | Particle Swarm Optimization | MMRE | Neural Networks.

The effort invested in a software project is probably one of the most important and mostanalyzed variables in recent years in the process of project management. The limitation ofalgorithmic effort prediction models is their inability to cope with uncertainties andimprecision surrounding software projects at the early development stage. More recentlyattention has turned to a variety of machine learning methods, and soft computing in particularto predict software development effort. Soft computing is a consortium of methodologiescentering in fuzzy logic, artificial neural networks and evolutionary computation. It isimportant to mention here that these methodologies are complementary and synergistic ratherthan competitive. They provide in one form or another flexible information processingcapability for handling real life ambiguous situations. These methodologies are currently usedfor reliable and accurate estimate of software development effort which has always been achallenge for both the software industry and academia. The aim of this study is to analyze softcomputing techniques in the existing models and to provide in depth review of software andproject estimation techniques existing in industry and literature based on the different testdatasets along with their strength and weaknesses.
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