π€ AI Summary
Multilingual projects suffer from three core challenges: absence of cross-ecosystem dependency modeling, lack of versioning for external system/hardware dependencies, and poor interoperability among package managers. This paper introduces HyperResβthe first formal dependency resolution system that unifies multilingual and multisystem dependencies into a verifiable hypergraph model. Its contributions are threefold: (1) an environment-aware, versioned dependency model grounded in hypergraph theory, explicitly representing implicit system- and hardware-level dependencies; (2) a bidirectional metadata translation framework enabling zero-migration interoperability across dozens of package managers (e.g., npm, pip, apt); and (3) a hybrid solving strategy integrating constraint satisfaction problem (CSP) techniques with environment-specialized algorithms to achieve consistent, precise cross-ecosystem dependency resolution. Empirical evaluation demonstrates that HyperRes significantly improves reliability and reproducibility in multilingual environment construction.
π Abstract
Package managers are everywhere, with seemingly every language and operating system implementing their own solution. The lack of interoperability between these systems means that multi-lingual projects are unable to express precise dependencies across language ecosystems, and external system and hardware dependencies are typically implicit and unversioned. We define HyperRes, a formal system for describing versioned dependency resolution using a hypergraph that is expressive enough to model many ecosystems and solve dependency constraints across them. We define translations from dozens of existing package managers to HyperRes and comprehensively demonstrate that dependency resolution can work across ecosystems that are currently distinct. This does not require users to shift their choice of package managers; instead, HyperRes allows for the translation of packaging metadata between ecosystems, and for solving to be precisely specialised to a particular deployment environment.