# Predicting the Objective and Priority of Issue Reports in Software Repositories

arXiv: https://arxiv.org/abs/2012.10951 · HTML: https://mlsanity.com/paper/2012.10951/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences](https://mlsanity.com/paper/2202.06689/) (2022)
- [Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study](https://mlsanity.com/paper/2304.12269/) (2023)
- [CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers](https://mlsanity.com/paper/2203.17196/) (2022)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [GiveMeLabeledIssues: An Open Source Issue Recommendation System](https://mlsanity.com/paper/2303.13418/) (2023)
- [Semantically-enhanced Topic Recommendation System for Software Projects](https://mlsanity.com/paper/2206.00085/) (2022)
- [Tag that issue: Applying API-domain labels in issue tracking systems](https://mlsanity.com/paper/2304.02877/) (2023)
- [GIRT-Model: Automated Generation of Issue Report Templates](https://mlsanity.com/paper/2402.02632/) (2024)
- [An Empirical Study on Data Leakage and Generalizability of Link Prediction Models for Issues and Commits](https://mlsanity.com/paper/2211.00381/) (2022)
- [Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models](https://mlsanity.com/paper/2506.00128/) (2025)
- [LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment](https://mlsanity.com/paper/2601.01780/) (2026)

## Abstract (arXiv)

Developers collaboratively discuss, implement, use, and share software entities hosted on software repositories. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team's effort. An issue report is a rich source of collaboratively curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub's top 1000 repositories do not have any labels. We aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and state-of-the-art text classifiers. To the best of our knowledge, we are the first to fine-tune a Transformer for issue classification. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with 82% and 75% accuracy, respectively. Moreover, we conducted human labeling and evaluation on unlabeled issues from six unseen GitHub projects to assess the performance of the cross-project model on new data. The model achieves 90% accuracy. We obtain 85% average Percent Agreement and 71% Randolph's free-marginal Kappa translating to substantial agreement among labelers.