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A Hybrid Technique using Grey Relational Analysis and Regression for Software Effort Estimation using Feature Selection

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Author(s): Geeta Nagpal | Moin Uddin | Arvinder Kaur

Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307

Volume: 1;
Issue: 6;
Start page: 20;
Date: 2012;
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Keywords: Estimation by Analogy | Feature Selection | Grey relational Analysis | Regression.

ABSTRACT
Software Estimation Techniques present an inclusive set of directives for software project developers, project managers and the management in order to produce more accurate estimates or predictions for future developments. The estimates also facilitate allocation of resources’ for Software development. Estimations also smooth the process of re-planning, prioritizing, classification and reuse of the projects. Various estimation models are widely being used in the Industry as well for research purposes. Several comparative studies have been executed on them, but choosing the best technique is quite intricate. Estimation by Analogy(EbA) is the method of making estimations based on the outcome from k most analogous projects. The projects close in distance are potentially similar to the reference project from the repository of projects. This method has widely been accepted and is quite popular as it impersonates human beings inherent judgment skill by estimating with analogous projects. In this paper, Grey Relational Analysis(GRA) is used as the method for feature selection and also for locating the closest analogous projects to the reference project from the set of projects. The closest k projects are then used to build regression models. Regression techniques like Multiple Linear Regression, Stepwise Regression and Robust regression techniques are used to find the effort from the closest projects.
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