一句话：提出结合自然语言处理与可解释机器学习的方法，在真实软件开发场景中自动将工单分配给正确团队。
本文关注全球分布式多学科敏捷团队中的工单分配问题：人工分配易出错，错误工单会造成资源浪费，并影响交付结果与项目成本。作者提出一种工单分析与自动团队分配方法，利用自然语言处理提取工单文本特征，并采用可解释机器学习模型进行预测。模型不仅输出团队或角色分配结果，还能提供决策解释，帮助理解分配依据。在真实世界团队分配任务上的结果表明，模型表现良好，可为敏捷开发中的工单路由与资源组织提供实用洞见。
In the context of software development, managing and organizing agile boards of multi-disciplinary teams distributed around the world is a great challenge, especially regarding the process of assigning tickets to the correct team roles. Incorrectly assigned tickets can result in significant resource waste in any project and directly influence delivery outcomes and project costs. This work proposes a method for ticket analysis and automatic team assignment using Natural Language Processing and explainable Machine Learning models. Results show that the models perform well on a real-world team assignment task and provide insights into their decision.
@inproceedings{paperbot7208,
title = {A real-world case study for automated ticket team assignment using natural language processing and explainable models},
author = {Lucas Marcondes Pavelski and Rodrigo de Souza Braga},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3561164}
}