Image de couverture de livre

The Unreasonable Effectiveness of Open Science in AI: A Replication Study

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Gundersen, Odd Erik, Cappelen, Odd, Mølnå, Martin, Nilsen, Nicklas Grimstad
Format:	Preprint
Publié:	2024
Sujets:	Artificial Intelligence Machine Learning Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Automated Reproducibility Has a Problem Statement Problem
par: Snelleman, Thijs, et autres
Publié: (2025)

On the Replicability and Reproducibility of Deep Learning in Software Engineering
par: Liu, Chao, et autres
Publié: (2020)

A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
par: Linåker, Johan, et autres
Publié: (2025)

Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
par: Irugalbandara, Chandra, et autres
Publié: (2023)

Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
par: Giamattei, Luca, et autres
Publié: (2024)

Predicting Open Source Software Sustainability with Deep Temporal Neural Hierarchical Architectures and Explainable AI
par: Karim, S M Rakib Ul, et autres
Publié: (2026)

Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects
par: Khatoonabadi, SayedHassan, et autres
Publié: (2023)

Generative AI to Generate Test Data Generators
par: Baudry, Benoit, et autres
Publié: (2024)

Leveraging XP and CRISP-DM for Agile Data Science Projects
par: Shimaoka, Andre Massahiro, et autres
Publié: (2025)

Effective Large Language Model Debugging with Best-first Tree Search
par: Song, Jialin, et autres
Publié: (2024)

Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization
par: Chen, Zhi, et autres
Publié: (2024)

OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research
par: Rahman, Musfiqur, et autres
Publié: (2025)

What's documented in AI? Systematic Analysis of 32K AI Model Cards
par: Liang, Weixin, et autres
Publié: (2024)

On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
par: Moussa, Rebecca, et autres
Publié: (2022)

AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime
par: Su, Jianhao, et autres
Publié: (2026)

Dissecting Transformers: A CLEAR Perspective towards Green AI
par: Jain, Hemang, et autres
Publié: (2025)

Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
par: González, Alexandra, et autres
Publié: (2024)

CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositories
par: Xiao, Yijia, et autres
Publié: (2025)

Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
par: Ahmed, Waqas, et autres
Publié: (2026)

A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
par: Raheem, Mira, et autres
Publié: (2025)

Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
par: Duan, Moming, et autres
Publié: (2023)

Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
par: Sorokin, Lev, et autres
Publié: (2024)

Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
par: Fehlis, Yao, et autres
Publié: (2026)

Optimizing AI-Assisted Code Generation
par: Torka, Simon, et autres
Publié: (2024)

Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
par: Jewitt, James, et autres
Publié: (2026)

Challenges and Paths Towards AI for Software Engineering
par: Gu, Alex, et autres
Publié: (2025)

Semantic-Preserving Transformations as Mutation Operators: A Study on Their Effectiveness in Defect Detection
par: Hort, Max, et autres
Publié: (2025)

Standardization Trends on Safety and Trustworthiness Technology for Advanced AI
par: Jeon, Jonghong
Publié: (2024)

The Impact of Environment Configurations on the Stability of AI-Enabled Systems
par: Rahman, Musfiqur, et autres
Publié: (2024)

Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents
par: Yang, Zhenning, et autres
Publié: (2025)

SETA: Statistical Fault Attribution for Compound AI Systems
par: Chowdhury, Sayak, et autres
Publié: (2026)

Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities
par: Afifi, Nehal, et autres
Publié: (2025)

Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
par: Kumar, Anand, et autres
Publié: (2025)

Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs
par: Patel, Harsh, et autres
Publié: (2024)

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations
par: Luo, Yujie, et autres
Publié: (2025)

asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit
par: Koch, Norman, et autres
Publié: (2025)

Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
par: Hu, Boyue Caroline, et autres
Publié: (2025)

On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance
par: Singh, Jaskirat, et autres
Publié: (2024)

Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java
par: Bandarupalli, Gopichand
Publié: (2025)

VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents
par: Xu, Bing, et autres
Publié: (2026)