Communities on GitHub often use issue labels as a way of triaging issues by assigning them priority ratings based on how urgently they should be addressed. The labels used are determined by the repository contributors and not standardised by GitHub. This makes it difficult for priority-related reasoning across repositories for both researchers and contributors. Previous work shows interest in how issues are labelled and what the consequences for those labels are. For instance, some previous work has used clustering models and natural language processing to categorise labels without a particular emphasis on priority. With this publication, we introduce a unique data set of 812 manually categorised labels pertaining to priority; normalised and ranked as low-, medium-, or high-priority. To provide an example of how this data set could be used, we have created a tool for GitHub contributors that will create a list of the highest priority issues from the repositories to which they contribute. We have released the data set and the tool for anyone to use on Zenodo because we hope that this will help the open source community address high-priority issues more effectively and inspire other uses.
Tue 16 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
16:00 - 18:00 | |||
16:00 15mTalk | MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery PROMISE 2024 Jafar Akhoundali Leiden University, Sajad Rahim Nouri Islamic Azad University of Ramsar, Kristian Rietveld Leiden University, Olga Gadyatskaya DOI | ||
16:15 15mTalk | A Pilot Study in Surveying Data Challenges of Automatic Software Engineering Tasks PROMISE 2024 DOI | ||
16:30 15mTalk | Prioritising GitHub Priority Labels PROMISE 2024 DOI | ||
16:45 15mTalk | Predicting Fairness of ML Software Configurations PROMISE 2024 Salvador Robles Herrera University of Texas at El Paso, Verya Monjezi University of Texas at El Paso, Vladik Kreinovich University of Texas at El Paso, Ashutosh Trivedi University of Colorado Boulder, Saeid Tizpaz-Niari University of Texas at El Paso DOI | ||
17:00 5mDay closing | Closing PROMISE 2024 |