AI 中文总结
本文针对开源项目中长期软件组件的开发人员分配问题，开发了基于Git提交建模专业技能的网页应用，该应用经测试在速度和目标开发人员排序上表现良好。
AI 中文摘要
通过GitHub开展的开源软件开发，让全球无数开发者得以参与软件项目开发。将合适的任务分配给合适的开发人员能让团队高效工作，若分配的任务不符合开发人员的专业技能，会导致完成时间变慢且代码可维护性降低。尽管已有不少针对特定工作的自动化分配研究，但针对新项目中专业技能特定的长期软件组件的开发人员分配研究较少。本文开发了一款网页应用，基于开发人员过往的Git提交内容为其专业技能建模，并自动将其分配到新项目中的最优任务。测试显示，系统速度随后端模型不同而变化，但服务器端数据缓存使最差情况速度提升了9.86倍；在最多47个可能的分配选项中，72.4%的任务的目标开发人员排在前10位。
英文摘要
Open-source software development through GitHub has enabled countless software projects to be developed by developers from across the world. Assigning the right task to the right developer enables teams to work efficiently. Assigning a task which is not representative of a developer's expertise results in slower completion time and less maintainable code. Whilst much work has been done to automate this assignment for specific jobs, there is little work addressing the assignment of developers to expertise-specific long-term components of a new project. This paper produces a web application that models developers' expertise based on their previous Git commits and automatically assigns them to an optimal task within a new project. Testing showed that the system's speed varies depending on the back-end model used but server-side data caching improved worst-case speeds by a factor of 9.86. 72.4% of tasks had their target developer listed in the top 10, out of a possible 47.
CommentsAccepted at the Conference on Advanced Artificial Intelligence and Education (CAAIE 2026). 11 pages, 5 figures
