Tootfinder

«5% of our systems is infrastructure as code. 95% is infrastructure as Powerpoint»
I've seen too much of that 😂
https://www.youtube.com/watch?v=rXPpkzdS-q4

Ethical Classification of Non-Coding Contributions in Open-Source Projects via Large Language Models
Sergio Cobos, Javier Luis C\'anovas Izquierdo
https://arxiv.org/abs/2507.21583

Ethical Classification of Non-Coding Contributions in Open-Source Projects via Large Language Models
The development of Open-Source Software (OSS) is not only a technical challenge, but also a social one due to the diverse mixture of contributors. To this aim, social-coding platforms, such as GitHub, provide the infrastructure needed to host and develop the code, but also the support for enabling the community's collaboration, which is driven by non-coding contributions, such as issues (i.e., change proposals or bug reports) or comments to existing contributions. As with any other social endea…

ARPaCCino: An Agentic-RAG for Policy as Code Compliance
Francesco Romeo, Luigi Arena, Francesco Blefari, Francesco Aurelio Pironti, Matteo Lupinacci, Angelo Furfaro
https://arxiv.org/abs/2507.10584

ARPaCCino: An Agentic-RAG for Policy as Code Compliance
Policy as Code (PaC) is a paradigm that encodes security and compliance policies into machine-readable formats, enabling automated enforcement in Infrastructure as Code (IaC) environments. However, its adoption is hindered by the complexity of policy languages and the risk of misconfigurations. In this work, we present ARPaCCino, an agentic system that combines Large Language Models (LLMs), Retrieval-Augmented-Generation (RAG), and tool-based validation to automate the generation and verificati…

Deployability-Centric Infrastructure-as-Code Generation: An LLM-based Iterative Framework
Tianyi Zhang, Shidong Pan, Zejun Zhang, Zhenchang Xing, Xiaoyu Sun
https://arxiv.org/abs/2506.05623

Deployability-Centric Infrastructure-as-Code Generation: An LLM-based Iterative Framework
Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templates from natural language descriptions, but current evaluation focuses on syntactic correctness while ignoring deployability, the fatal measure of IaC template utility. We address this gap through two contributions: (1) IaCGen…

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
Henri A\"idasso, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2507.04173

Efficient Detection of Intermittent Job Failures Using Few-Shot Learning
One of the main challenges developers face in the use of continuous integration (CI) and deployment pipelines is the occurrence of intermittent job failures, which result from unexpected non-deterministic issues (e.g., flaky tests or infrastructure problems) rather than regular code-related errors such as bugs. Prior studies developed machine-learning (ML) models trained on large datasets of job logs to classify job failures as either intermittent or regular. As an alternative to costly manual …