## 1 Introduction  
When the \"Agile Manifesto\" was published on 11-13 February 2001 [1], it marked a pivotal moment in the software development landscape. Traditional software development methods, including the waterfall model, v model, incremental model, and iterative model, were prevalent. These traditional methods, while prevalent, often fell short in accommodating the rapid changes and collaborative needs of modern software projects. Software organizations recognized the limitations of traditional methods, such as long development cycles, limited customer involvement, and difficulty in managing large projects. This recognition led them to embark on a paradigm shift towards Agile methodologies. Agile methodologies offered a more adaptable and responsive approach, meeting the evolving needs of software development. The aforementioned challenges, along with late defect detection, limited adaptability to change, rigidity, and stakeholder dissatisfaction, further fueled the shift towards Agile methodologies [2, 3]. The transition to Agile, characterized by its flexibility, iterative development, and customer-centricity, emerged as a solution to address the aforementioned challenges facing traditional software development methods and improve overall software development efficiency. Shortcomings like long development cycles often led to project failures. Meanwhile, the limited adaptability to change hindered innovation and competitive advantages. High costs were incurred due to the late detection of defects and the difficulty in managing large projects. Stakeholder dissatisfaction and risk management challenges underscored the need for a more dynamic and responsive approach. Agile methodologies, with their iterative development and focus on customer involvement, aimed to significantly reduce project failures and costs, enhance adaptability to change, foster innovation, and boost stakeholder satisfaction. As a result, many software organizations gradually shifted their development methodologies to embrace Agile, recognizing its potential to mitigate the damages associated with traditional approaches [4,5]. Despite transitioning to Agile as a solution to overcome the challenges and shortcomings of traditional software development methods, software organizations still face significant delays in project timelines and cost overruns. They also face critical issues that need to be addressed. Numerous studies in the literature highlight that most software projects struggle with timeline overruns and frequently exceed budgetary constraints. A McKinsey and University of Oxford study covering 5400 large-scale \(\Pi\) projects revealed alarming statistics: On average, projects ran 66% over budget and 33% overtime [6]. In a parallel vein, another study [7], this time scrutinizing 1471 software projects, indicated that one in six experienced a staggering budget overrun of 200% and a schedule overrun of nearly 70%. Additionally, the project cost management statistics for 2023 [8] revealed that 47% of Agile projects experienced delays or budget overruns or lead to customer dissatisfaction. Moreover, 11% of Agile projects faced outright failure, ultimately delivering no software product. These findings underscore the pervasive challenges in project management, accentuating the pivotal role of accurate effort estimation in ensuring timely completion and adherence to budget constraints in software projects. In ASD projects, particularly during the inception of each sprint in sprint planning, the Scrum team employs expert-based estimation methods such as planning poker, T-shirt sizing, the Fibonacci sequence, the bucket system, and more to assess the complexity and effort associated with user stories or issues slated for development. However, these estimations are inherently susceptible to inaccuracies, as they heavily rely on individual knowledge and past experiences in evaluating the intricacies of similar stories or issues from prior projects.