A Novel PMFFC -Based Software Effort Estimation Using FMGKF-DENN Algorithm
Keywords:Software, Effort estimation, project management, feature extraction, Sea Turtle Foraging Optimization (STFO), Neural Network (NN), Deviation
Software Effort Estimation (SEE) is getting more concerned owing to the software industry’s development. An increase in the deadline along with the budget of the project was led by an incorrect estimation. This may fail the project in turn. Using the Fisher and Mish Gaussian Kernel Function based Deep Elman Neural Network (FMGKF-DENN) algorithm, a novel Pearson and Mahalanobis- centered Farthest First Clustering (PMFFC)-centered SEE was proposed in this study. Primarily, through determining specific factors like scope, objectives, Infrastructure, and characteristics, the details regarding the provided historical projects are obtained. For efficient task completion, the projects are grouped utilizing the PMFFC algorithm grounded on the details gathered. Next, the classes split the code for those specific types of projects. After that, the input data is pre-processed. Certain features are retrieved as of the pre-processed data and the Controlled Sea Turtle Foraging Optimization (CSTFO) approach chooses the crucial features. The deviation value is deemed as a target for efficient SEE in the FMGKF-DENN, where the selected features are fed. The experiential outcomes illustrated that the SEE process was executed more accurately by the proposed framework along with outperforming various top-notch models.
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