A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects

• URL: http://arxiv.org/abs/2604.27532v1
• Date: Thu, 30 Apr 2026 07:36:57 GMT
• Title: A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects
• Abstract summary: Open-source software projects widely adopt "good first issue" (GFI) labels to highlight beginner-friendly tasks.<n>This study analyzes 406,826 issues and 1,117 newcomer GFI pull requests across 37 popular GitHub repositories over a four-year period.<n>We find that while the proportion of issues with GFI labels remained stable during the first three years, it underwent a statistically significant decline beginning in January 2024.
• Score: 2.385829414517223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-source software (OSS) projects rely on effective newcomer onboarding to sustain their communities. OSS projects widely adopt "good first issue" (GFI) labels to highlight beginner-friendly tasks. As development practices continue to evolve, understanding how these onboarding mechanisms change over time is important for both maintainers and researchers. This study analyzes 406,826 issues and 1,117 newcomer GFI pull requests across 37 popular GitHub repositories (30 of which use GFI labels) over a four-year period from July 2021 to June 2025. We find that while the proportion of issues with GFI labels remained stable during the first three years, it underwent a statistically significant decline beginning in January 2024, with substantial variation across projects not explained by repository age or programming language. Despite this supply-side decline, newcomer engagement with GFI issues remains stable at approximately 27%, suggesting that GFI labels maintain consistent attractiveness. Examining the outcomes of this engagement, we find that the merge rate of newcomer GFI pull requests declined from 61.9% to 42.2%. Initial pull request characteristics such as description length and code size show no significant association with merge outcomes, indicating that success is not predicted by the quantitative characteristics of the initial submission alone. Together, these findings reveal a widening gap between stable newcomer interest in GFIs and the declining availability and success of GFI-based onboarding, underscoring the need for maintainers to sustain both GFI labeling and review support.

Related papers

• Do Good, Stay Longer? Temporal Patterns and Predictors of Newcomer-to-Core Transitions in Conventional OSS and OSS4SG [3.2952032218821565]
  OSS4SG projects retain contributors at 2.2X higher rates and contributors have 19.6% higher probability of achieving core status.<n>Our findings suggest that finding a project aligned with personal values and taking time to understand the before major contributions are key strategies for achieving core status.
  arXiv  Detail & Related papers  (2026-01-30T16:30:25Z)
• Governance Matters: Lessons from Restructuring the data.table OSS Project [7.308586458018547]
  Data.table is a high-performance R package widely adopted in production analytics pipelines.<n>Before the reform, data.table faced a growing backlog of unresolved issues and open pull requests.<n>Following the reform, the project experienced a 200% increase in new contributor recruitment, a drop in pull request resolution time from over 700 days to under a week, and a 3x increase in contributor retention.
  arXiv  Detail & Related papers  (2026-01-19T23:41:18Z)
• A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems [2.1215369927766714]
  This paper analyzes over 42K unique commits and over 4.7K resolved issues across eight leading systems.<n>Our analysis identifies three distinct development profiles: sustained, steady, and burst-driven.<n>Data about issues shows that the most frequent concerns involve bugs (22%), infrastructure (14%), and agent coordination challenges (10%).
  arXiv  Detail & Related papers  (2026-01-12T02:07:15Z)
• An Empirical Analysis of Community and Coding Patterns in OSS4SG vs. Conventional OSS [1.6821922667774782]
  Open Source Software for Social Good (OSS4SG) projects aim to address critical societal challenges.<n>We compare community structure, contributor engagement, and coding practices of OSS4SG and conventional OSS projects.<n>Our findings reveal that OSS4SG projects foster significantly more stable and "sticky" communities, whereas conventional OSS projects are more "magnetic" (75.4%), attracting a high turnover of contributors.
  arXiv  Detail & Related papers  (2026-01-06T21:37:12Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• Benchmarking Reasoning Robustness in Large Language Models [76.79744000300363]
  We find significant performance degradation on novel or incomplete data.<n>These findings highlight the reliance on recall over rigorous logical inference.<n>This paper introduces a novel benchmark, termed as Math-RoB, that exploits hallucinations triggered by missing information to expose reasoning gaps.
  arXiv  Detail & Related papers  (2025-03-06T15:36:06Z)
• Classification or Prompting: A Case Study on Legal Requirements Traceability [4.629156733452248]
  Legal requirements traceability is a key task where engineers must analyze technical requirements against target artifacts.<n>In this paper, we investigate two automated solutions based on language models, including large ones (LLMs)<n>The first solution, Kashif, is a classifier that leverages sentence transformers and semantic similarity.<n>The second solution, RICE_LRT, prompts a recent generative LLM based on RICE, a prompt engineering framework.
  arXiv  Detail & Related papers  (2025-02-07T13:33:40Z)
• GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [83.72561905487447]
  We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.<n>GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.<n>It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
  arXiv  Detail & Related papers  (2025-02-03T07:04:29Z)
• Personalized First Issue Recommender for Newcomers in Open Source Projects [12.444293953494032]
  We analyze 37 newcomers and their first issues resolved across multiple projects. We find that the first issues resolved by the same newcomer share similarities in task type, programming language, and project domain. We propose a Personalized First Issue Recommender (PFIRec) to rank candidate issues for a given newcomer by leveraging the identified influential features.
  arXiv  Detail & Related papers  (2023-08-17T15:19:13Z)
• Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation [135.80439360370556]
  We propose a diversity-enhancing generative network (DEG-Net) for the FHA problem. It can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC)
  arXiv  Detail & Related papers  (2023-07-12T06:29:02Z)
• Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training [60.892342868936865]
  Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. We propose a data heterogeneous-robust FL approach, FedGSP, to address this challenge. We show that FedGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-01-31T03:15:28Z)
• Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis [61.740077541531726]
  We provide a new central and open leaderboard for any KGQA benchmark dataset as a focal point for the community. Our analysis highlights existing problems during the evaluation of KGQA systems.
  arXiv  Detail & Related papers  (2022-01-20T13:46:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.