Software Cost Estimation Technique Based On Multiple Artificial Neural Network Models

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Wathq Asmael Hamed

Abstract

Software cost estimation is an essential and important endeavor for the effective implementation of applications development project concerning its price & time plus its direction concerning its monitoring of autonomous applications development jobs. Software cost estimation is the prediction of software development endeavor and applications development time necessary to create a software job. The scheduling is of scheduling Resources, Budget, Time and several equally Precise software cost estimation is regarded as a tricky job as the information concerning the application project to be designed in the time of its beginning and completion remains obscure, thus drives the investigators from both professors and business to research in the exact same. What's more, it's always preferable for any approximation version to be inclusive because precision in estimation versions mutually lies together using their inclusiveness. So software cost estimation procedure being predictive in character hence requires for inclusiveness that will consequently bring inside that the precision. Within this paper, we'll present many versions for software cost estimation according to variants from Artificial Neural Networks which were completed within the research study. One of those models relies on exact choice of drivers as input into an Artificial Neural Network. And others derive from hybrids of Artificial Neural Networks with distinct Meta-heuristic algorithms as utilization of meta-heuristics in forecast issues such as that of program cost estimation is becoming more popularity. Everyone these versions have been experimented with variety of valid data collections.    

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How to Cite
Hamed, W. A. . (2021). Software Cost Estimation Technique Based On Multiple Artificial Neural Network Models. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(4), 615–624. Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/544
Section
Research Articles