Cover Image

An Empirical Study of Policy-as-Code Adoption in Open-Source Software Projects

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Foalem, Patrick Loic, Khomh, Foutse, Da Silva, Leuson, Merlo, Ettore
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
by: Foalem, Patrick Loic, et al.
Published: (2025)

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges
by: Foalem, Patrick Loic, et al.
Published: (2026)

Exploring Security Practices in Infrastructure as Code: An Empirical Study
by: Verdet, Alexandre, et al.
Published: (2023)

Performance Smells in ML and Non-ML Python Projects: A Comparative Study
by: Belias, François, et al.
Published: (2025)

A Taxonomy of Inefficiencies in LLM-Generated Python Code
by: Abbassi, Altaf Allah, et al.
Published: (2025)

LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges
by: Da Silva, Leuson, et al.
Published: (2024)

Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning
by: P, Akilesh, et al.
Published: (2026)

ReCatcher: Towards LLMs Regression Testing for Code Generation
by: Abbassi, Altaf Allah, et al.
Published: (2025)

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
by: Shahedi, Kaveh, et al.
Published: (2025)

Continuously Learning Bug Locations
by: Mindom, Paulina Stevia Nouwou, et al.
Published: (2024)

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software
by: Bhatia, Aaditya, et al.
Published: (2023)

Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
by: Olewicki, Doriane, et al.
Published: (2024)

Protecting Privacy in Software Logs: What Should Be Anonymized?
by: Aghili, Roozbeh, et al.
Published: (2024)

BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
by: Ma, Yinghang, et al.
Published: (2025)

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy
by: Taraghi, Mina, et al.
Published: (2026)

Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
by: Imani, Aaron, et al.
Published: (2024)

Structural Anchors and Reasoning Fragility:Understanding CoT Robustness in LLM4Code
by: Liu, Yang, et al.
Published: (2026)

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
by: Shah, Mehil B., et al.
Published: (2024)

Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
by: Majdinasab, Vahid, et al.
Published: (2024)

Bugs in Large Language Models Generated Code: An Empirical Study
by: Tambon, Florian, et al.
Published: (2024)

Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
by: Morovati, Mohammad Mehdi, et al.
Published: (2023)

Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
by: Shah, Mehil B, et al.
Published: (2024)

RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
by: Oueslati, Khouloud, et al.
Published: (2025)

Role of CI Adoption in Mobile App Success: An Empirical Study of Open-Source Android Projects
by: Zhou, Xiaoxin, et al.
Published: (2026)

Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
by: Piao, Yonnel Chen Kuang, et al.
Published: (2025)

An Empirical Study of Security-Policy Related Issues in Open Source Projects
by: Kanaji, Rintaro, et al.
Published: (2025)

From Technical Excellence to Practical Adoption: Lessons Learned Building an ML-Enhanced Trace Analysis Tool
by: Shahedi, Kaveh, et al.
Published: (2025)

Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
by: Liu, Yang, et al.
Published: (2025)

Unveiling Code Clone Patterns in Open Source VR Software: An Empirical Study
by: Chen, Huashan, et al.
Published: (2025)

Fault Localization in Deep Learning-based Software: A System-level Approach
by: Morovati, Mohammad Mehdi, et al.
Published: (2024)

Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects
by: Kupari, Alvari, et al.
Published: (2026)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
by: Aghili, Roozbeh, et al.
Published: (2025)

On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
by: Jahromi, Ali Soltanian Fard, et al.
Published: (2026)

Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
by: Majdinasab, Vahid, et al.
Published: (2025)

Improving the Robustness of Large Language Models for Code Tasks via Fine-tuning with Perturbed Data
by: Liu, Yang, et al.
Published: (2026)

An Empirical Study of Generative AI Adoption in Software Engineering
by: Giray, Görkem, et al.
Published: (2025)

A Survey of Bugs in AI-Generated Code
by: Gao, Ruofan, et al.
Published: (2025)

Measuring Software Development Waste in Open-Source Software Projects
by: Varanasi, Dhiraj SM, et al.
Published: (2024)