Enhancing Software Effort Estimation through Reinforcement Learning-based Project Management-Oriented Feature Selection

TL;DR

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 and demonstrates that the proposed approach achieves more precise estimation compared to traditional methods.

Abstract

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.

Figures (7)

• Figure 1: Random Forest Algorithm
• Figure 2: MARLFS Algorithm
• Figure 3: Comparison of proposed functions.
• Figure 4: Comparison of the derivatives of proposed functions.
• Figure 5: Plot of Spearman correlation analysis among SEERA dataset.