Issue Auto-Assignment in Software Projects with Machine Learning Techniques
This work addresses the issue assignment problem for managers and technical leaders in software projects, but it is incremental as it builds on existing literature with an industrial report.
The paper tackled the problem of manual issue assignment in software projects, which is error-prone and time-consuming, by comparing different machine learning algorithms in an industrial setting at a global electronics company, resulting in minimized time and errors in the assignment process.
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.