Tootfinder

Detection of Performance Changes in MooBench Results Using Nyrki\"o on GitHub Actions
Shinhyung Yang, David Georg Reichelt, Henrik Ingo, Wilhelm Hasselbring
https://arxiv.org/abs/2510.11310

Detection of Performance Changes in MooBench Results Using Nyrkiö on GitHub Actions
In GitHub with its 518 million hosted projects, performance changes within these projects are highly relevant to the project's users. Although performance measurement is supported by GitHub CI/CD, performance change detection is a challenging topic. In this paper, we demonstrate how we incorporated Nyrkiö to MooBench. Prior to this work, Moobench continuously ran on GitHub virtual machines, measuring overhead of tracing agents, but without change detection. By adding the upload of the measur…

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan
https://arxiv.org/abs/2510.09316 https://

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering. In this work, we--for the first time--have compiled an extensive l…

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Mohammad Baqar
https://arxiv.org/abs/2510.08667 https://

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with FAISS-based vector search to deliver context-aware ticket resolution recommendations. The approach embeds historical JIRA tickets, user comm…

AI-assisted Programming May Decrease the Productivity of Experienced Developers by Increasing Maintenance Burden
Feiyang (Amber), Xu, Poonacha K. Medappa, Murat M. Tunc, Martijn Vroegindeweij, Jan C. Fransoo
https://arxiv.org/abs/2510.10165

AI-assisted Programming May Decrease the Productivity of Experienced Developers by Increasing Maintenance Burden
Generative AI solutions like GitHub Copilot have been shown to increase the productivity of software developers. Yet prior work remains unclear on the quality of code produced and the challenges of maintaining it in software projects. If quality declines as volume grows, experienced developers face increased workloads reviewing and reworking code from less-experienced contributors. We analyze developer activity in Open Source Software (OSS) projects following the introduction of GitHub Copilot.…

Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
Spandan Garg, Ben Steenhoek, Yufan Huang
https://arxiv.org/abs/2510.08996 https://

Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
Current benchmarks for evaluating software engineering agents, such as SWE-Bench Verified, are predominantly derived from GitHub issues and fail to accurately reflect how developers interact with chat-based coding assistants in integrated development environments (IDEs). We posit that this mismatch leads to a systematic overestimation of agent's capabilities in real-world scenarios, especially bug fixing. We introduce a novel benchmarking framework that transforms existing formal benchmarks int…

NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
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Can You Trust Your Copilot? A Privacy Scorecard for AI Coding Assistants
Amir AL-Maamari
https://arxiv.org/abs/2509.20388 https://arxiv.org/pdf/2509.20388

Can You Trust Your Copilot? A Privacy Scorecard for AI Coding Assistants
The rapid integration of AI-powered coding assistants into developer workflows has raised significant privacy and trust concerns. As developers entrust proprietary code to services like OpenAI's GPT, Google's Gemini, and GitHub Copilot, the unclear data handling practices of these tools create security and compliance risks. This paper addresses this challenge by introducing and applying a novel, expert-validated privacy scorecard. The methodology involves a detailed analysis of four document ty…

Who Do You Think You Are? Creating RSE Personas from GitHub Interactions
Felicity Anderson, Julien Sindt, Neil Chue Hong
https://arxiv.org/abs/2510.05390 https://

Who Do You Think You Are? Creating RSE Personas from GitHub Interactions
We describe data-driven RSE personas: an approach combining software repository mining and data-driven personas applied to research software (RS), an attempt to describe and identify common and rare patterns of Research Software Engineering (RSE) development. This allows individuals and RS project teams to understand their contributions, impact and repository dynamics - an important foundation for improving RSE. We evaluate the method on different patterns of collaborative interaction behaviour…

Cryogenic Materials Repository: A Public Resource and New Measurements for Cryogenic Research Applications
Henry E. Nachman (Department of Physics, The University of Texas at Austin, Weinberg Institute for Theoretical Physics, Texas Center for Cosmology and Astroparticle Physics, Austin, TX, USA), Oorie Desai (Department of Physics, The University of Texas at Austin, Weinberg Institute for Theoretical Physics, Texas Center for Cosmology and Astroparticle Physics, Austin, TX, USA), Nich…

Cryogenic Materials Repository: A Public Resource and New Measurements for Cryogenic Research Applications
Low-temperature systems play a vital role in a variety of scientific research applications, including the next generation of cosmology and astrophysics telescopes. More ambitious cryogenic applications require precise estimates of the thermal conductivity of materials and thermal joints to meet project goals. We present the development of the Cryogenic Material Repository (CMR), a public GitHub repository of cryogenic material properties data created to support and enable researchers across sci…

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source
Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey
https://arxiv.org/abs/2509.16295

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source
Open digital public infrastructure needs community management to ensure accountability, sustainability, and robustness. Yet open-source projects often rely on centralized decision-making, and the determinants of successful community management remain unclear. We analyze 637 GitHub repositories to trace transitions from founder-led to shared governance. Specifically, we document trajectories to community governance by extracting institutional roles, actions, and deontic cues from version-control…

Pedagogically Motivated and Composable Open-Source RISC-V Processors for Computer Science Education
Ian McDougall, Harish Batchu, Michael Davies, Karthikeyan Sankaralingam
https://arxiv.org/abs/2509.20514

Pedagogically Motivated and Composable Open-Source RISC-V Processors for Computer Science Education
While most instruction set architectures (ISAs) are only available to use through the purchase of a restrictive commercial license, the RISC-V ISA presents a free and open-source alternative. Due to this availability, many free and open-source implementations have been developed and can be accessed on platforms such as GitHub. If an open source, easy-to-use, and robust RISC-V implementation could be obtained, it could be easily adapted for pedagogical and amateur use. In this work we accomplish…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/7]:
- Understanding Prompt Management in GitHub Repositories: A Call for Best Practices
Hao Li, Hicham Masri, Filipe R. Cogo, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan

An Empirical Study of Security-Policy Related Issues in Open Source Projects
Rintaro Kanaji, Brittany Reid, Yutaro Kashiwa, Raula Gaikovina Kula, Hajimu Iida
https://arxiv.org/abs/2510.05604

An Empirical Study of Security-Policy Related Issues in Open Source Projects
GitHub recommends that projects adopt a SECURITY.md file that outlines vulnerability reporting procedures. However, the effectiveness and operational challenges of such files are not yet fully understood. This study aims to clarify the challenges that SECURITY.md files face in the vulnerability reporting process within open-source communities. Specifically, we classified and analyzed the content of 711 randomly sampled issues related to SECURITY.md. We also conducted a quantitative comparative …

humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems
Andrii Kliachkin, Jana Lep\v{s}ov\'a, Gilles Bareilles, Jakub Mare\v{c}ek
https://arxiv.org/abs/2509.21254

humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems
There has been a considerable interest in constrained training of deep neural networks (DNNs) recently for applications such as fairness and safety. Several toolkits have been proposed for this task, yet there is still no industry standard. We present humancompatible.train (https://github.com/humancompatible/train), an easily-extendable PyTorch-based Python package for training DNNs with stochastic constraints. We implement multiple previously unimplemented algorithms for stochastically constra…

AUDDT: Audio Unified Deepfake Detection Benchmark Toolkit
Yi Zhu, Heitor R. Guimar\~aes, Arthur Pimentel, Tiago Falk
https://arxiv.org/abs/2509.21597 https://

AUDDT: Audio Unified Deepfake Detection Benchmark Toolkit
With the prevalence of artificial intelligence (AI)-generated content, such as audio deepfakes, a large body of recent work has focused on developing deepfake detection techniques. However, most models are evaluated on a narrow set of datasets, leaving their generalization to real-world conditions uncertain. In this paper, we systematically review 28 existing audio deepfake datasets and present an open-source benchmarking toolkit called AUDDT (https://github.com/MuSAELab/AUDDT). The goal of thi…

RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms
Samah Kansab, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2510.04796 https://…

RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms
Empirical research on code review processes is increasingly central to understanding software quality and collaboration. However, collecting and analyzing review data remains a time-consuming and technically intensive task. Most researchers follow similar workflows - writing ad hoc scripts to extract, filter, and analyze review data from platforms like GitHub and GitLab. This paper introduces RevMine, a conceptual tool that streamlines the entire code review mining pipeline using large language…

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
Simin Chen, Yixin He, Suman Jana, Baishakhi Ray
https://arxiv.org/abs/2509.25894 https://…

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch GitHub issues and use backend LLMs to generate patches that fix the reported bugs. However, existing work primarily focuses on the functional correctness of APR-generated patches, whether they pass hidden or regression tests, while largely ignoring potential security risks. Given the openness of platforms like GitHub, where any user can raise issues an…

Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
Viktoria Stray, Elias Goldmann Brandtz{\ae}g, Viggo Tellefsen Wivestad, Astri Barbala, Nils Brede Moe
https://arxiv.org/abs/2509.20353

Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
This study investigates the real-world impact of the generative AI (GenAI) tool GitHub Copilot on developer activity and perceived productivity. We conducted a mixed-methods case study in NAV IT, a large public sector agile organization. We analyzed 26,317 unique non-merge commits from 703 of NAV IT's GitHub repositories over a two-year period, focusing on commit-based activity metrics from 25 Copilot users and 14 non-users. The analysis was complemented by survey responses on their roles and p…

CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
Daniele Bifolco, Guido Annicchiarico, Pierluigi Barbiero, Massimiliano Di Penta, Fiorella Zampetti
https://arxiv.org/abs/2510.01077

CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
Large Language Models (LLMs) are widely used in software development tasks nowadays. Unlike reusing code taken from the Web, for LLMs' generated code, developers are concerned about its lack of trustworthiness and possible copyright or licensing violations, due to the lack of code provenance information. This paper proposes CodeGenLink, a GitHub CoPilot extension for Visual Studio Code aimed at (i) suggesting links containing code very similar to automatically generated code, and (ii) whenever …

When Shared Worlds Break: Demystifying Defects in Multi-User Extended Reality Software Systems
Shuqing Li, Chenran Zhang, Binchang Li, Cuiyun Gao, Michael R. Lyu
https://arxiv.org/abs/2510.01182

When Shared Worlds Break: Demystifying Defects in Multi-User Extended Reality Software Systems
Multi-user Extended Reality (XR) systems enable transformative shared experiences but introduce unique software defects that compromise user experience. Understanding software defects in multi-user XR systems is crucial for enhancing system reliability, yet remains underexplored. To fill the gap, this paper presents the first large-scale empirical study of multi-user XR defects, analyzing 2,649 real-world bug reports from diverse sources, including developer forums, GitHub repositories, and app…

Demystifying the Evolution of Neural Networks with BOM Analysis: Insights from a Large-Scale Study of 55,997 GitHub Repositories
Xiaoning Ren, Yuhang Ye, Xiongfei Wu, Yueming Wu, Yinxing Xue
https://arxiv.org/abs/2509.20010

Demystifying the Evolution of Neural Networks with BOM Analysis: Insights from a Large-Scale Study of 55,997 GitHub Repositories
Neural networks have become integral to many fields due to their exceptional performance. The open-source community has witnessed a rapid influx of neural network (NN) repositories with fast-paced iterations, making it crucial for practitioners to analyze their evolution to guide development and stay ahead of trends. While extensive research has explored traditional software evolution using Software Bill of Materials (SBOMs), these are ill-suited for NN software, which relies on pre-defined mod…

On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
Miku Watanabe, Hao Li, Yutaro Kashiwa, Brittany Reid, Hajimu Iida, Ahmed E. Hassan
https://arxiv.org/abs/2509.14745

On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
Large language models (LLMs) are increasingly being integrated into software development processes. The ability to generate code and submit pull requests with minimal human intervention, through the use of autonomous AI agents, is poised to become a standard practice. However, little is known about the practical usefulness of these pull requests and the extent to which their contributions are accepted in real-world projects. In this paper, we empirically study 567 GitHub pull requests (PRs) gen…

GitHub's Copilot Code Review: Can AI Spot Security Flaws Before You Commit?
Amena Amro, Manar H. Alalfi
https://arxiv.org/abs/2509.13650 https://arxiv.…

GitHub's Copilot Code Review: Can AI Spot Security Flaws Before You Commit?
As software development practices increasingly adopt AI-powered tools, ensuring that such tools can support secure coding has become critical. This study evaluates the effectiveness of GitHub Copilot's recently introduced code review feature in detecting security vulnerabilities. Using a curated set of labeled vulnerable code samples drawn from diverse open-source projects spanning multiple programming languages and application domains, we systematically assessed Copilot's ability to identify a…

From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem
James Jewitt, Hao Li, Bram Adams, Gopi Krishnan Rajbahadur, Ahmed E. Hassan
https://arxiv.org/abs/2509.09873

From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem
Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks, exposing organizations to potential litigation and users to undisclosed risk. However, the field lacks a data-driven understanding of how frequently these conflicts occur, where they originate, and which communities are most affected. We present the first end-to-end audit of licenses for datasets and models on Hugging Face, as well as their downstream integration into open-source software applications…