利用大型语言模型预测软件工程项目的成本与工期
Leveraging Large Language Models for Predicting Cost and Duration in Software Engineering Projects
AI总结：
针对软件项目成本与工期估算难题，本文评估LLM相对传统及机器学习方法的准确性、易用性与工业适用性，证明其可提供更准确且易用的预测替代方案。
摘要：
准确估算项目成本和工期仍是软件工程中的一项关键挑战，直接影响预算编制与资源管理。传统估算技术虽被广泛采用，但因其复杂性以及软件开发项目的动态特性，往往存在不足。本研究提出一种使用大型语言模型（LLM）的创新方法，以提高项目成本预测的准确性和可用性。我们考察LLM相对于传统方法和当代机器学习技术的效能，重点关注其简化估算流程并提供更高准确性的潜力。本研究围绕若干关键问题展开：LLM能否超越现有模型、其融入当前实践的难易程度、能否优于传统估算，以及传统方法为何仍在工业环境中占主导。通过将LLM应用于一系列真实世界数据集，并将其性能与最先进方法和传统方法进行比较，本研究旨在证明LLM不仅能产生更准确的估算，还能作为复杂预测模型的一种用户友好替代方案，从而有可能变革软件行业的项目管理策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
