AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
In this study, it is asked whether large language models can preserve cross-artifact consistency under change in a round-trip workflow.
This paper proposes a dependency-aware incremental migration framework that elevates the unit of translation from individual files to dependency-consistent batches and improves scalability and reliability in repository-level code translation.
This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.
This research focuses on Maven configuration and structure updates and explores how the CodeT5 model can automate the migration of Java 8 projects to Java 17, finding the model accelerates parts of migration but remains unsuitable for fully automated use.
This work introduces RepoProbe, a novel benchmark for evaluating repository-level code understanding through open-ended Q&A using GitHub Discussions, which focuses on open-ended architectural inquiries rather than defect reporting and proposes a Checklist-Based Verification Protocol that decomposes answers into atomic,...
This study investigates whether a frontier LLM can generate Dockerfiles and Docker Compose configurations for multi-service applications using repository contents without access to developer-authored deployment artifacts and analytically derives a minimal explicit deployment specification for information that cannot be...
This work constructs an evaluation framework from 400 BigCodeBench problems by injecting controlled AST-level corruptions into reference solutions, giving each repair task a known minimal patch, and positions edit fidelity as a distinct axis of code-repair quality and shows that it can be measured and learned.
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