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.

翻译：目的：本研究旨在探讨数据要素市场在软件项目管理中的应用，重点通过解决传统方法面临的挑战来改进工作量估算。设计/方法论/方法：本研究提出一种基于特征选择的解决方案，利用数据要素市场和基于强化学习的算法来提高软件工作量估算的准确性。探究了MARLFS算法的应用，并对算法和奖励函数进行了定制化改进。研究结果：本研究表明，与传统方法相比，所提出的方法通过利用特征选择指导软件开发中的项目管理，实现了更精确的估算。原创性/价值：本研究通过提供一种结合数据要素市场、机器学习及特征选择的新颖方法，为改进软件工作量估算做出了贡献，克服了传统方法的局限性，并为未来项目管理研究提供了见解。