Empirical Analysis of Pull Requests for Google Summer of Code

• URL: http://arxiv.org/abs/2412.13120v2
• Date: Tue, 14 Jan 2025 18:55:03 GMT
• Title: Empirical Analysis of Pull Requests for Google Summer of Code
• Authors: Saheed Popoola,
• Abstract summary: The Google Summer of Code (GSoC) is a global initiative that matches students or new contributors with experienced mentors to work on open-source projects. This study presents an empirical analysis of pull requests created by interns during the GSoC program.
• Score: 0.0
• License:
• Abstract: Internship and industry-affiliated capstone projects are popular ways to expose students to real world experiences and bridge the gap between academic training and industry requirements. However, these two approaches often require active industry collaboration, and many students struggle to find industry placements. Open-source contributions are a crucial alternative to gain real world experience, earn publicly verifiable contribution with real-world impact, and learn from experienced open-source contributors. The Google Summer of Code (GSoC) is a global initiative that matches students or new contributors with experienced mentors to work on open-source projects. The program aims to introduce the students to open-source development, help them gain valuable skills under the guidance of mentors, and hopefully encourage them to continue contributing to open-source projects. The realization of the program objectives will provide a continuous pool of talented new contributors necessary for maintaining open-source projects. This study presents an empirical analysis of pull requests created by interns during the GSoC program. We extracted and analyzed 17,232 pull requests from 2,456 interns across 1,937 open-source projects. The results show most tasks involve both code-intensive activities like adding new features and fixing bugs, as well as non-code tasks like updating documentation and restructuring the codebase. Feedback from reviewers covers code functionality and programming logic, testing coverage, error handling, code readability, and adherence to best practices. Finally, we discuss the implications of these results for software engineering education.

Related papers

• Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors [29.04639728020965]
  We propose a novel agent workflow, Trace-and-Verify (TRAVER), which combines knowledge tracing to estimate a student's knowledge state and turn-by-turn verification to ensure effective guidance toward task completion. Experiments reveal the challenges of coding tutoring and demonstrate that TRAVER achieves a significantly higher success rate.
  arXiv  Detail & Related papers  (2025-02-18T22:13:00Z)
• OSSDoorway: A Gamified Environment to Scaffold Student Contributions to Open Source Software [15.46529088040852]
  This paper proposes and evaluates OSSDoorway, a tool designed to guide students contributing to open source software (OSS) projects. We recruited 29 students and administered a self-efficacy questionnaire before and after their use of OSSDoorway. Results show that OSSDoorway boosts students' self-efficacy and provides a structured, gamified learning experience.
  arXiv  Detail & Related papers  (2025-02-11T22:07:27Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models [95.96734086126469]
  Large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern, which has been widely studied in existing work. We perform a detailed coarse-to-fine analysis of the inference performance of various code libraries.
  arXiv  Detail & Related papers  (2024-04-17T15:57:50Z)
• Bridging Theory to Practice in Software Testing Teaching through Team-based Learning (TBL) and Open Source Software (OSS) Contribution [3.190574537106449]
  This paper presents a teaching approach for a software testing course that integrates theory and practical experience. The paper reports on our experience implementing the pedagogical approach over four consecutive semesters of a Software Testing course within an undergraduate Software Engineering program.
  arXiv  Detail & Related papers  (2024-04-16T21:16:17Z)
• DevEval: Evaluating Code Generation in Practical Software Projects [52.16841274646796]
  We propose a new benchmark named DevEval, aligned with Developers' experiences in practical projects. DevEval is collected through a rigorous pipeline, containing 2,690 samples from 119 practical projects. We assess five popular LLMs on DevEval and reveal their actual abilities in code generation.
  arXiv  Detail & Related papers  (2024-01-12T06:51:30Z)
• Experiential Co-Learning of Software-Developing Agents [83.34027623428096]
  Large language models (LLMs) have brought significant changes to various domains, especially in software development. We introduce Experiential Co-Learning, a novel LLM-agent learning framework. Experiments demonstrate that the framework enables agents to tackle unseen software-developing tasks more effectively.
  arXiv  Detail & Related papers  (2023-12-28T13:50:42Z)
• Collaborative, Code-Proximal Dynamic Software Visualization within Code Editors [55.57032418885258]
  This paper introduces the design and proof-of-concept implementation for a software visualization approach that can be embedded into code editors. Our contribution differs from related work in that we use dynamic analysis of a software system's runtime behavior. Our visualization approach enhances common remote pair programming tools and is collaboratively usable by employing shared code cities.
  arXiv  Detail & Related papers  (2023-08-30T06:35:40Z)
• Code Recommendation for Open Source Software Developers [32.181023933552694]
  CODER is a novel graph-based code recommendation framework for open source software developers. Our framework achieves superior performance under various experimental settings, including intra-project, cross-project, and cold-start recommendation.
  arXiv  Detail & Related papers  (2022-10-15T16:40:36Z)
• Dive into Deep Learning [119.30375933463156]
  The book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large.
  arXiv  Detail & Related papers  (2021-06-21T18:19:46Z)
• Representation of Developer Expertise in Open Source Software [12.583969739954526]
  We use the World of Code infrastructure to extract the complete set of APIs in the files changed by open source developers. We then employ Doc2Vec embeddings for vector representations of APIs, developers, and projects. We evaluate if these embeddings reflect the postulated topology of the Skill Space.
  arXiv  Detail & Related papers  (2020-05-20T16:36:07Z)

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.