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Evolutionary Computing Techniques for Software Effort Estimation


Affiliations
1 I.K.G. Punjab Technical University, Jalandhar, Punjab, India
2 Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
 

Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.

Keywords

Machine Learning, COCOMO, MMRE, Evolutionary Computing.
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  • Evolutionary Computing Techniques for Software Effort Estimation

Abstract Views: 347  |  PDF Views: 185

Authors

Sumeet Kaur Sehra
I.K.G. Punjab Technical University, Jalandhar, Punjab, India
Yadwinder Singh Brar
I.K.G. Punjab Technical University, Jalandhar, Punjab, India
Navdeep Kaur
Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India

Abstract


Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.

Keywords


Machine Learning, COCOMO, MMRE, Evolutionary Computing.

References