# SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub

arXiv: https://arxiv.org/abs/2501.15922 · HTML: https://mlsanity.com/paper/2501.15922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [A no-regret generalization of hierarchical softmax to extreme multi-label classification](https://mlsanity.com/paper/1810.11671/) (2018)
- [Efficacy of Synthetic Data as a Benchmark](https://mlsanity.com/paper/2409.11968/) (2024)
- [Understanding Skills for OSS Communities on GitHub](https://mlsanity.com/paper/2209.02222/) (2022)
- [Does Synthetic Data Make Large Language Models More Efficient?](https://mlsanity.com/paper/2310.07830/) (2023)
- [Can I Solve It? Identifying APIs Required to Complete OSS Task](https://mlsanity.com/paper/2103.12653/) (2021)
- [A Bug or a Suggestion? An Automatic Way to Label Issues](https://mlsanity.com/paper/1909.00934/) (2019)
- [GiveMeLabeledIssues: An Open Source Issue Recommendation System](https://mlsanity.com/paper/2303.13418/) (2023)
- [Attracting and Retaining OSS Contributors with a Maintainer Dashboard](https://mlsanity.com/paper/2202.07740/) (2022)
- [Tag that issue: Applying API-domain labels in issue tracking systems](https://mlsanity.com/paper/2304.02877/) (2023)

## Built on by (most cited first, 1 of 1)

- [Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models](https://mlsanity.com/paper/2506.00128/) (2025)

## Abstract (arXiv)

New contributors often struggle to find tasks that they can tackle when onboarding onto a new Open Source Software (OSS) project. One reason for this difficulty is that issue trackers lack explanations about the knowledge or skills needed to complete a given task successfully. These explanations can be complex and time-consuming to produce. Past research has partially addressed this problem by labeling issues with issue types, issue difficulty level, and issue skills. However, current approaches are limited to a small set of labels and lack in-depth details about their semantics, which may not sufficiently help contributors identify suitable issues. To surmount this limitation, this paper explores large language models (LLMs) and Random Forest (RF) to predict the multilevel skills required to solve the open issues. We introduce a novel tool, SkillScope, which retrieves current issues from Java projects hosted on GitHub and predicts the multilevel programming skills required to resolve these issues. In a case study, we demonstrate that SkillScope could predict 217 multilevel skills for tasks with 91% precision, 88% recall, and 89% F-measure on average. Practitioners can use this tool to better delegate or choose tasks to solve in OSS projects.