  Purpose: The study aims to investigate the application of the data element
market in software project management, focusing on improving effort estimation
by addressing challenges faced by traditional methods.
Design/methodology/approach: This study proposes a solution based on feature
selection, utilizing the data element market and reinforcement learning-based
algorithms to enhance the accuracy of software effort estimation. It explores
the application of the MARLFS algorithm, customizing improvements to the
algorithm and reward function. Findings: This study demonstrates that the
proposed approach achieves more precise estimation compared to traditional
methods, leveraging feature selection to guide project management in software
development. Originality/value: This study contributes to the field by offering
a novel approach that combines the data element market, machine learning, and
feature selection to improve software effort estimation, addressing limitations
of traditional methods and providing insights for future research in project
management.
