TaskAllocator: A Recommendation Approach for Role-based Tasks Allocation in Agile Software Development

TL;DR

TaskAllocator is a role-based recommendation system for task assignment in agile projects, improving allocation efficiency by predicting suitable team roles for incoming tasks, and is validated through case studies and benchmarking.

Contribution

The paper introduces TaskAllocator, a novel role-based task recommendation approach for agile teams, with a practical JIRA plugin and publicly available source code.

Findings

Outperforms existing machine learning models in role prediction accuracy.

Effectively integrates with JIRA for real-time task assignment suggestions.

Validated on ten real-world agile projects.

Abstract

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.