NLP4IP: Natural Language Processing-based Recommendation Approach for Issues Prioritization
Saad Shafiq, Atif Mashkoor, Christoph Mayr‐Dorn, Alexander F. Egyed · 2021
This paper proposes a recommendation approach for issues (e.g., a story, a bug, or a task) prioritization based on natural language processing, called NLP4IP. The proposed semi-automatic approach takes into account the priority and story points attributes of existing issues defined by the project stakeholders and devises a recommendation model capable of dynamically predicting the rank of newly added or modified issues. NLP4IP was evaluated on 19 projects from 6 repositories employing the JIRA issue tracking software with a total of 29,698 issues. A comprehensive benchmark study was also conducted to compare the performance of various machine learning models. The results of the study showed an average top@3 accuracy of 81% and a mean squared error of 2.2 when evaluated on the validation set. The applicability of the proposed approach is demonstrated in the form of a JIRA plug-in illustrating predictions made by the newly developed machine learning model. The dataset has also been made publicly available in order to support other researchers working in this domain.