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Journal of Software Engineering
  Year: 2011 | Volume: 5 | Issue: 1 | Page No.: 38-48
DOI: 10.3923/jse.2011.38.48
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A Fine Parameter Tuning for COCOMO 81 Software Effort Estimation using Particle Swarm Optimization

CH.V.M.K. Hari and P.V.G.D. Prasad Reddy

The basic goal of project planning is to look into the future, identify the activities that need to be done to complete the project successfully and plan scheduling and resource allocation for these activities. Software effort estimation is the most important activity in project planning. So far many models are proposed by using machine learning algorithms, but no model is proved successful for efficiently and consistently predicting the effort. In this study we proposed two models using particle swarm optimization (PSO) with Constriction Factor for fine tuning of parameters of the constructive cost model (COCOMO) effort estimation. The models deals efficiently with imprecise and uncertain input and enhances the reliability of software effort estimation. The experimental part of the study illustrates the approach and contrast it with the standard numeric version of the COCOMO, standard singal variable models, Triangular Membership Function and Gbell function Models.
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  •    Scheduling Quality Related Activities in Incremental Software Development Projects Based on Monte Carlo Simulation
  •    Research on Learning Bayesian Networks by Particle Swarm Optimization
  •    PSO-Based Controller Design for Rotary Inverted Pendulum System
  •    Candidate Process Models for Component Based Software Development
  •    Regression Testing Method Based on XML Schema for GUI Components
How to cite this article:

CH.V.M.K. Hari and P.V.G.D. Prasad Reddy, 2011. A Fine Parameter Tuning for COCOMO 81 Software Effort Estimation using Particle Swarm Optimization. Journal of Software Engineering, 5: 38-48.

DOI: 10.3923/jse.2011.38.48






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