Accurate software work estimates is essential to the planning, management, and execution of a successful project on schedule and within budget. The necessity for accurate software work estimates is something that will never go away since both overestimation and underestimate provide substantial barriers to the development of additional software (SEE). Research and practise are aimed at finding the machine learning estimating technique that is most successful for a given set of criteria and data. This is the goal of the research and practise. Most academics working in a particular subject are not aware of the findings of previous studies that investigated different approaches to effort estimate in machine learning. The primary purpose of this investigation is to aid researchers working in the field of software development by assisting them in determining which method of machine learning produces the most promising effort estimate accuracy prediction.


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    Titel :

    Software Effort Estimation using Machine Learning Algorithms


    Beteiligte:
    Shah, Ruju (Autor:in) / Shah, Vrunda (Autor:in) / Nair, Anuja R. (Autor:in) / Vyas, Tarjni (Autor:in) / Desai, Shivani (Autor:in) / Degadwala, Sheshang (Autor:in)


    Erscheinungsdatum :

    01.12.2022


    Format / Umfang :

    1014266 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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