A real-world case study for automated ticket team assignment using natural language processing and explainable models

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.