[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118807-en":3,"doc-seo-118807-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118807,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","An Intelligent Framework for Estimating Software Development Projects using Machine Learning - Research overview","Software effort and cost estimation is a persistent challenge in software engineering because early-stage planning depends on uncertain inputs such as requirements changes, platform shifts, project size, budget limits, and complexity. After effort is estimated, cost assessment follows and benefits both customers and developers. The paper reviews algorithmic, expert judgment, analogy-based, and machine learning estimation approaches while addressing the uncertainty that affects estimation accuracy. It proposes a modular machine learning framework that is independent of algorithmic models, offering interpretability, learning ability, and robustness under imprecise data.","An Intelligent Framework for Estimating Software Development Projects using Machine Learning  \nPrateek Srivastava1, Nidhi Srivastava2, Rashi Agarwal3 and Pawan Singh4  \n1,2 Amity Institute of Information Technology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India  \n3Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, India 4Department of Computer Science and Engineering, ASET, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India  \n[1](1 prateeksri976@gmail.com)[ prateeksri976@gmail.com](1 prateeksri976@gmail.com) , [2](2 nsrivastava2@lko.amity.edu)[ nsrivastava2@lko.amity.edu](2 nsrivastava2@lko.amity.edu) ,[3](3 dr.rashiagrawal@gmail.com)[ dr.rashiagrawal@gmail.com](3 dr.rashiagrawal@gmail.com), [4](4 psingh10@lko.amity.edu)[ psingh10@lko.amity.edu](4 psingh10@lko.amity.edu)  \nAbstract: The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogybased estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs.  \nKeywords: Software Engineering, Software Project Estimation, Machine Learning, Effort and Cost Estimation.  \nI. INTRODUCTION  \nEstimating software projects is a critical and challenging aspect of software development that can be extremely complicated. It is difficult to make an accurate assessment of software development during the early stages of a project. This is due to the many uncertainties associated with inputs such as changes in requirements, platform changes, size of the project, budget constraints, complexity, etc. To meet the competitive demands of today's industry, it is imperative to estimate software effort early in the development process. The procedure of software effort estimation consists of estimating the amount of effort that will be required to finish a particular software project based on the amount of time required. Several studies in the literature have used interchangeably the terms\"software effort estimation\" and \"estimation of software costs\" . In contrast, the estimation of software costs is a direct result of software effort estimation [1] .  \nThere is a growing need for updated, reliable, high-quality software that is easy to use, inexpensive, and delivered in a short period of time. Hence, it is the client's or developer's responsibility to perform a cost-benefit analysis. An analysis of the estimation is converted into dollars. Since the demand for software effort estimation has increased in the industry, it has become a critical task to be performed during the early stages of development. Successful software project management relies heavily on accurate effort estimations [2] . Overestimating and underestimating depend on the allocation  \nof resources as overestimating is the allocation of excessive resources, and underestimating is the allocation of insufficient resources. The ability to predict effort accurately allows risks to be reduced.  \nAmong the branches of AI, machine learning is significant. It has been widely used since 1991 to estimate the development effort of ","cbCaioCUdPjAbcuZ","https://ap.wps.com/l/cbCaioCUdPjAbcuZ","pdf",412366,1,10,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"Why is software effort and cost estimation challenging at the early stage?\",\"answer\":\"Accurate assessment is difficult because early inputs are uncertain, including changes in requirements, platform updates, project size, budget constraints, and complexity.\"},{\"question\":\"Which estimation approaches are discussed in the paper?\",\"answer\":\"The paper covers algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, including their different aspects.\"},{\"question\":\"What is the purpose of the proposed machine learning framework?\",\"answer\":\"The framework aims to improve estimation accuracy for software development projects by providing high interpretability, learning capability, and robustness to imprecise and uncertain inputs, using a modular architecture.\"}]","An Intelligent Framework for Estimating Software Development Projects using Machine Learning - 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