Tuning of COCOMO Model Parameters by using Bee Colony Optimization

The objective of the current research is applying bee colony optimization (BCO) meta-heuristic approach to optimize the parameters of constructive cost model (COCOMO) for improving software cost estimation. The BCO is a new branch of swarm intelligence and has been applied successfully to various en...

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Bibliographic Details
Published in:Indian journal of science and technology 2015-07, Vol.8 (14), p.1-1
Main Authors: Chalotra, Sherry, Sehra, S. K., Brar, Y. S., Kaur, Navdeep
Format: Article
Language:English
Subjects:
Bees
Colonies
Combinatorial analysis
Computer programs
Mathematical models
Optimization
Software
Tuning
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Summary:The objective of the current research is applying bee colony optimization (BCO) meta-heuristic approach to optimize the parameters of constructive cost model (COCOMO) for improving software cost estimation. The BCO is a new branch of swarm intelligence and has been applied successfully to various engineering disciplines. BCO approach is a "bottom-up" approach to modeling where special kinds of artificial agents are created by analogy with bees. These artificial agents or bees are used to solve complex combinatorial optimization problems. The proposed model validation is carried out using interactive voice response software project dataset of a company. The results generated by the proposed model are compared to those obtained by methods proposed in the literature using Walston-Felix, SEL, Bailey-Basil, COCOMO II and Halstead models. The BCO approach generates various partial solutions and the best solution is selected based on mean magnitude of relative error. The results obtained show that the proposed BCO based model is able to improve the accuracy of cost estimation and also outperform other models.
ISSN:0974-6846
0974-5645
DOI:10.17485/ijst/2015/v8i14/70010