Software Effort Estimation Using Deep Learning: A Gentle Review

Accurately estimating the effort required for software development projects is critical to effective resource allocation and project planning. Recent advancements in deep learning techniques have shown promise in enhancing software effort estimation by identifying intricate patterns in software engineering data. This comprehensive systematic review delves into the existing methodologies and optimization approaches for software effort estimation using deep learning. The study encompasses an extensive literature search, comparative analysis, and identification of research gaps. The paper covers many topics, including deep learning techniques, relative analysis parameters, and performance metrics. The review compares optimization algorithms based on their strengths, weaknesses, datasets, and performance metrics. Additionally, the paper discusses the limitations of the reviewed studies and the comparative analysis process. The findings highlight the implications for software effort estimation, future research directions, and recommendations for practitioners. Overall, this review contributes to our understanding of deep learning-based optimization in software effort estimation, providing valuable guidance for researchers and practitioners in selecting appropriate methodologies for precise analysis.