# Developer Prioritization in Bug Repositories

arXiv: https://arxiv.org/abs/1704.04764 · HTML: https://mlsanity.com/paper/1704.04764/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2017.

## Built on by (most cited first, 7 of 7)

- [AI Techniques for Software Requirements Prioritization](https://mlsanity.com/paper/2108.00832/) (2021)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [A Comparative Study of Transformer-based Neural Text Representation Techniques on Bug Triaging](https://mlsanity.com/paper/2310.06913/) (2023)
- [Towards Utility-based Prioritization of Requirements in Open Source Environments](https://mlsanity.com/paper/2102.08638/) (2021)
- [DapStep: Deep Assignee Prediction for Stack Trace Error rePresentation](https://mlsanity.com/paper/2201.05256/) (2022)
- [Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems](https://mlsanity.com/paper/2204.12893/) (2022)
- [TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings](https://mlsanity.com/paper/2508.16860/) (2025)

## Abstract (arXiv)

Developers build all the software artifacts in development. Existing work has studied the social behavior in software repositories. In one of the most important software repositories, a bug repository, developers create and update bug reports to support software development and maintenance. However, no prior work has considered the priorities of developers in bug repositories. In this paper, we address the problem of the developer prioritization, which aims to rank the contributions of developers. We mainly explore two aspects, namely modeling the developer prioritization in a bug repository and assisting predictive tasks with our model. First, we model how to assign the priorities of developers based on a social network technique. Three problems are investigated, including the developer rankings in products, the evolution over time, and the tolerance of noisy comments. Second, we consider leveraging the developer prioritization to improve three predicted tasks in bug repositories, i.e., bug triage, severity identification, and reopened bug prediction. We empirically investigate the performance of our model and its applications in bug repositories of Eclipse and Mozilla. The results indicate that the developer prioritization can provide the knowledge of developer priorities to assist software tasks, especially the task of bug triage.