TaskAllocator: A Recommendation Approach for Role-based Tasks Allocation in Agile Software Development
This addresses task allocation inefficiencies for project managers in agile teams, but it is incremental as it builds on existing recommendation and machine learning methods.
The paper tackles the problem of allocating tasks to roles in agile software development by proposing TaskAllocator, a recommendation approach that predicts task assignments to roles rather than individuals, evaluated on ten case study projects with benchmark comparisons to contemporary machine learning models.
In this paper, we propose a recommendation approach -- TaskAllocator -- in order to predict the assignment of incoming tasks to potential befitting roles. The proposed approach, identifying team roles rather than individual persons, allows project managers to perform better tasks allocation in case the individual developers are over-utilized or moved on to different roles/projects. We evaluated our approach on ten agile case study projects obtained from the Taiga.io repository. In order to determine the TaskAllocator's performance, we have conducted a benchmark study by comparing it with contemporary machine learning models. The applicability of the TaskAllocator was assessed through a plugin that can be integrated with JIRA and provides recommendations about suitable roles whenever a new task is added to the project. Lastly, the source code of the plugin and the dataset employed have been made public.