Semantic Soundness for Language Interoperability

TL;DR

This paper introduces a semantic framework for verifying sound language interoperability after compilation, enabling safe data conversions between languages with complex semantics and practical implementation considerations.

Contribution

It proposes a novel interoperation-after-compilation approach with formal verification, addressing real-world language interoperability challenges.

Findings

Framework for specifying data conversions between languages

Semantic model of source types as sets of target terms

Case studies demonstrating practical applicability

Abstract

Programs are rarely implemented in a single language, and thus questions of type soundness should address not only the semantics of a single language, but how it interacts with others. Even between type-safe languages, disparate features frustrate interoperability, as invariants from one language can easily be violated in the other. In their seminal 2007 paper, Matthews and Findler proposed a multi-language construction that augments the interoperating languages with a pair of boundaries that allow code from one language to be embedded in the other. While the technique has been widely applied, their syntactic source-level interoperability doesn't reflect practical implementations, where behavior of interaction is defined after compilation to a common target, and any safety must be ensured by target invariants or inserted target-level "glue code." In this paper, we present a framework for the design and verification of sound language interoperability that follows an interoperation-after-compilation strategy. Language designers specify what data can be converted between types of the languages via a relation $\tau_A \sim \tau_B$ and specify target glue code implementing conversions. Then, by giving a semantic model of source types as sets of target terms, we can establish soundness of conversions: i.e., whenever $\tau_A \sim \tau_B$, the corresponding pair of conversions convert target terms that behave as $\tau_A$ to target terms that behave as $\tau_B$, and vice versa. We can then prove semantic type soundness for the entire system. We illustrate our framework via a series of case studies that demonstrate how our semantic interoperation-after-compilation approach allows us both to account for complex differences in language semantics and make efficiency trade-offs based on particularities of compilers or targets.