Agile Software Development Effort Estimation: A Comprehensive Survey

This survey paper examines the evolution of effort and cost estimation methodologies in Agile software development, with a focus on Serum-based projects. Traditional techniques such as Story Points and Function Points are evaluated against modern approaches that integrate machine learning (ML), fuzzy logic, and optimization algorithms. The paper synthesizes findings from various studies, including those by Arora et al. (2018), Owais and Ramakishore (2016), and Dave et al. (2021), to assess the effectiveness of these methods in improving estimation accuracy. It also considers the influence of human factors, team dynamics, and customer involvement on project success, as highlighted by Tam et al. (2020) and Meckenstock (2024). The survey identifies a trend towards data-driven estimation models, as confirmed by Fernandez-Diego et al. (2020), and suggests that future research should explore hybrid ML models and deep learning algorithms to further refine Software Cost Estimation (SCE) processes. The paper contributes to the literature by providing a comprehensive overview of the current state of Agile estimation practices and by outlining directions for future research to enhance the precision and applicability of these techniques. This abstract encapsulates the essence of the survey, summa- rizing the research question, the approach taken, the key findings, and the significance of the work within the field of Agile software development.