University of Bahrain
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Optimization of COCOMO II model to Estimate software cost using Squirrel Algorithm

Show simple item record A. FADHIL, ANFAL 2021-08-13T18:04:52Z 2021-08-13T18:04:52Z 2021-08-13
dc.identifier.issn 2210-142X
dc.description.abstract Estimating software effort important highly role of project management which enables Estimate software cost. Inexact results may reason to increment or decrement in estimating effort. A lot of technique concerning software effort prediction was has applied for enhancing models that build optimal prediction accuracy. One of these techniques is Swarm intelligence. The process of choosing an optimal prediction algorithm depends on experts and complexity. The proposed paper enhances the prediction hire the COCOMO idea by executing the Squirrel Search Algorithm(SSA). The search behavior in squirrels is dependent on dynamic jumping and gliding techniques by applying the algorithm on four data sets (NASA 93, NASA 60, NASA 63, Bailey and Basili 18) and assess with the use of Mean Magnitude of Relative Error(MMRE). The score shows that the Squirrel search model has obtained best the coefficient values of the COCOMO model than Algorithms applied in this field. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject The Squirrel Algorithm en_US
dc.subject cost Estimation en_US
dc.subject Constructive Cost Mode en_US
dc.subject seasonal constant en_US
dc.subject NASA dataset en_US
dc.title Optimization of COCOMO II model to Estimate software cost using Squirrel Algorithm en_US
dc.contributor.authorcountry Mosul, Iraq en_US
dc.contributor.authoraffiliation Software of College of Computer Science and Mathematics, University of Mosul en_US
dc.source.title International Journal Of Computing and Digital System en_US
dc.abbreviatedsourcetitle IJCDS en_US

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