Accurate estimation of project costs and durations remains a pivotal challenge in software engineering, directly impacting budgeting and resource management. Traditional estimation techniques, although widely utilized, often fall short due to their complexity and the dynamic nature of software development projects. This study introduces an innovative approach using Large Language Models (LLMs) to enhance the accuracy and usability of project cost predictions. We explore the efficacy of LLMs against traditional methods and contemporary machine learning techniques, focusing on their potential to simplify the estimation process and provide higher accuracy. Our research is structured around critical inquiries into whether LLMs can outperform existing models, the ease of their integration into current practices, outperform traditional estimation, and why traditional methods still prevail in industry settings. By applying LLMs to a range of real-world datasets and comparing their performance to both state-of-the-art and conventional methods, this study aims to demonstrate that LLMs not only yield more accurate estimates but also offer a user-friendly alternative to complex predictive models, potentially transforming project management strategies within the software industry.

翻译：在软件工程中，准确估算项目成本与工期仍是关键挑战，直接影响预算制定与资源管理。尽管传统估算技术被广泛采用，但由于其复杂性及软件开发项目的动态特性，这些方法往往效果不佳。本研究提出一种创新方法，利用大型语言模型（LLMs）提升项目成本预测的准确性与实用性。我们探究了LLMs相对于传统方法及当代机器学习技术的效能，重点关注其简化估算流程与提供更高准确性的潜力。研究围绕以下核心问题展开：LLMs能否超越现有模型、其与当前实践结合的便捷性、是否优于传统估算方法，以及传统方法为何仍在工业场景中占据主导地位。通过将LLMs应用于多组真实数据集，并将其性能与前沿方法及传统方法进行比较，本研究旨在证明LLMs不仅能提供更精确的估算结果，还能作为复杂预测模型的用户友好型替代方案，有望推动软件行业的项目管理策略变革。