基于机器学习技术的软件项目问题自动分配
Issue Auto-Assignment in Software Projects with Machine Learning Techniques
摘要：
通常，软件项目中的经理或技术负责人手动分配问题。随着问题描述更加详细，此任务可能会变得更加复杂。这种复杂性也可能使该过程更容易出错（误分配）和耗时。在文献中，许多研究旨在通过使用机器学习策略来解决这个问题。虽然没有适用于所有公司的特定解决方案，但经验报告可用于指导工业自动分配项目的选择。本文介绍了一个全球性的电子公司，旨在尽量减少所花费的时间和错误，可能会出现在问题分配过程中进行的工业倡议。作为主要贡献，我们提出了一个文献综述，工业报告比较不同的算法，并在项目中吸取的经验教训。
英文摘要：
Usually, managers or technical leaders in software projects assign issues manually. This task may become more complex as more detailed is the issue description. This complexity can also make the process more prone to errors (misassignments) and time-consuming. In the literature, many studies aim to address this problem by using machine learning strategies. Although there is no specific solution that works for all companies, experience reports are useful to guide the choices in industrial auto-assignment projects. This paper presents an industrial initiative conducted in a global electronics company that aims to minimize the time spent and the errors that can arise in the issue assignment process. As main contributions, we present a literature review, an industrial report comparing different algorithms, and lessons learned during the project.
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。
