Estimation of Software Development Effort: A Differential Evolution Approach

Several software effort estimation techniques based on mathematical formulations have been proposed, however there is little consensus on the best technique for effort estimation, which can predict the estimated effort optimally for any given project. Much research has been carried out in finding ou...

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Bibliographic Details
Published in:Procedia computer science 2020, Vol.167, p.2643-2652
Main Authors: Singal, Prerna, Kumari, A Charan, Sharma, Prabha
Format: Article
Language:English
Subjects:
Algorithmic models
CoCoMo
CoCoMo II
Differential Evolution
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Summary:Several software effort estimation techniques based on mathematical formulations have been proposed, however there is little consensus on the best technique for effort estimation, which can predict the estimated effort optimally for any given project. Much research has been carried out in finding out the most suitable parameter values for accurate effort estimation. This research investigates the efficacy of Differential Evolution algorithms in improving the parameter values for algorithmic models like CoCoMo and CoCoMo II. Parameter values were obtained by using the three successful mutation strategies in Differential Evolution. The proposed methodology was tested on two datasets from the Promise Repository. Test results were compared with the original CoCoMo and CoCoMo II models based on MMRE. The proposed differential evolution approach predicted the estimated effort more accurately than the original models for the two datasets under consideration.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.03.343