Typesimilarity

Typesimilarity is a measure used in type systems and related domains to quantify how closely two types resemble each other. It is not a strict notion of equality but a gradient that can reflect compatibility, potential for interchangeability, or shared structure. The concept is particularly relevant in type inference, generic programming, refactoring, and data interoperability, where recognizing similar types can reduce manual annotations and support safe transformations.

Two broad perspectives influence typesimilarity. Structural similarity compares the concrete shape of types, such as field names, arities, and method signatures in records or objects. Nominal similarity relies on declared names and type aliases or subtyping relationships. Etiologically related types, such as a type and its subtypes, or two types that share a common ancestor, can also be deemed similar under a subtyping or inheritance framework. In practice, many systems combine structural and nominal cues, sometimes with probabilistic or heuristic weighting.

Common methods to quantify similarity include structural comparisons (graph or tree representations of type definitions, measuring overlap in fields or parameters), subtyping distance (shortest path in a type hierarchy), and embedding-based approaches (mapping types to vectors and computing cosine similarity). Additional techniques consider behavioral compatibility, such as compatible method contracts or polymorphic usage.

Applications span code analysis and tooling, including type inference guidance, safe refactoring, API compatibility checks, and schema mapping in data integration. Limitations arise from context-dependence: similarity may vary by language rules (static vs dynamic typing), the level of abstraction, and the intended use, making similarity a practical but not universal notion.