Artificial Intelligence Techniques to Enhance Cost, Effort, and Schedule Estimates in Software Development Projects: A Systematic Literature Review

Accurate estimates of cost, effort, and schedule in software development projects are essential for organizational success. This study presents a Systematic Literature Review (SLR) investigating the application of Artificial Intelligence (AI) techniques to improve the precision of such estimates. A total of 105 articles published between 2015 and 2024 were analyzed, retrieved from databases such as IEEE Digital Library, ACM Digital Library, SpringerLink, and ScienceDirect. The findings highlight that the most commonly used techniques include artificial neural networks, optimization-based methods, and advanced statistical models. The study identifies key challenges such as data quality, dataset limitations, and computational constraints, while also proposing recommendations for future research. It concludes that AI techniques show strong potential for enhancing the accuracy of software project estimates, particularly when integrated with agile methodologies. This research contributes to advancing the understanding of AI techniques in the field of software estimation, offering insights for future studies and the development of more effective tools.