Martim Monis, Martin Schwaighofer, Nuno Saavedra, Alexandra Mendes, René Mayrhofer, João F. Ferreira · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.24712 · cited by 0 works (OpenAlex) · open-access PDF
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Machine translation (Meta M2M100); the English original follows.
Implicit dependencies, uncontrolled network access, and other build-environment impurities create intransparent software supply chains. Identifying every dependency affecting a build is a difficult task. Functional package management addresses this through reproducible packaging, ensuring software relies exclusively on declared, hermetically isolated inputs. However, creating build recipes remains complex and time-consuming, posing a significant barrier to adoption for developers and maintainers. We present Vibenix, an open-source AI-powered agent that bridges this gap by automatically generating Nix packaging expressions. Vibenix uses a large language model (LLM) to iteratively refine recipes through a rule-guided build-repair feedback loop. On 466 packaging tasks from our new NixBench dataset, Vibenix produced buildable packages in 95.3% of cases using Claude Haiku 4.5, compared with 5.6% for a state-of-the-art LLM-based single-shot packaging tool. Manual validation of 100 packages revealed that 47% of the buildable packages were initially functionally correct; a post-build refinement process based on the evaluator-optimizer pattern increased functional correctness by 35 percentage points. On our new NixBench-Repair dataset of package maintenance tasks, Vibenix achieved an 87.6% success rate. These results demonstrate how unreliable LLMs can generate rigorously defined, complete, auditable dependency trees for real-world software, reducing maintainer burden while providing a trustworthy foundation for secure software distribution.
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Topics: Computer science, Software engineering, Dependency (UML), Correctness, Debugging, Software
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