Tue 16 Jul 2024 16:30 - 16:45 at Acerola - Afternoon session 2

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 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

16:00 - 18:00
Afternoon session 2PROMISE 2024 at Acerola
16:00
15m
Talk
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
15m
Talk
A Pilot Study in Surveying Data Challenges of Automatic Software Engineering Tasks
PROMISE 2024
Liming Dong CSIRO’s Data61, Qinghua Lu Data61, CSIRO, Liming Zhu CSIRO’s Data61
DOI
16:30
15m
Talk
Prioritising GitHub Priority Labels
PROMISE 2024
James Caddy University of Adelaide, Christoph Treude Singapore Management University
DOI
16:45
15m
Talk
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
5m
Day closing
Closing
PROMISE 2024