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@article{183618,
        author = {Ankit Kumar and Dr. Shashiraj Teotia},
        title = {TO EVALUATE THE DEVELOPED MODELS USING SELECTED SOFTWARE AND PERFORM A COST-BENEFIT ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2574-2586},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183618},
        abstract = {Accurate software cost estimation is crucial for effective project planning and resource management. This study compares three estimation models—COCOMO II, Decision Tree Forest (DTF), and Neuro-Fuzzy models—using benchmark datasets such as COCOMO’81 and ISBSG. Each model is evaluated for predictive accuracy and practical applicability using selected software tools.The findings indicate that Neuro-Fuzzy models provide superior accuracy, especially in handling uncertain data, but involve higher implementation and maintenance costs. COCOMO II, while less accurate in complex scenarios, is easier to implement and interpret, making it suitable for projects with well-defined parameters. DTF models strike a balance between accuracy and computational efficiency.A detailed cost-benefit analysis highlights the trade-offs between estimation precision and operational overhead, helping practitioners choose the most suitable model based on project needs and constraints.},
        keywords = {Software Cost Estimation, COCOMO II, Decision Tree Forest, Neuro-Fuzzy Models, Cost-Benefit Analysis, Software Tools},
        month = {August},
        }