whetstone_DSL/LANGUAGE_SUPPORT_ROADMAP.md

Language Support Roadmap

This document proposes an order of language support additions based on semantic fit for AST-driven workflows, ecosystem impact, and projection complexity. It is intended for future sprint planning (not tied to Sprint 8).

Principles

• Semantic Fit: Languages with clean, well-defined ASTs and predictable semantics.
• Ecosystem Impact: Practical value from large ecosystems and real-world usage.
• Projection Complexity: How hard it is to preserve semantics across languages.

Proposed Phases

Phase A: High-Impact Ecosystem Coverage

1. PHP (core language)

   • Rationale: Large installed base, web dominance, WordPress ecosystem.
   • Complexity: Medium (dynamic typing, mixed paradigms).
   • Notes: Add WordPress stubs and semantic tags after core PHP support.

2. C#

   • Rationale: Enterprise usage, strong typing, tooling clarity.
   • Complexity: Medium (runtime / reflection surface).

3. Kotlin

   • Rationale: JVM + Android, modern semantics, good tooling.
   • Complexity: Medium (interop with Java).

Phase B: Semantic-First Languages (Best AST Fit)

4. Julia

   • Rationale: Homoiconic macros + high-performance numeric core; strong AST fit.
   • Complexity: Medium (metaprogramming + multiple dispatch semantics).
   • Notes: Prioritize ML/numerical API mappings after core language support.

5. Common Lisp / Scheme

   • Rationale: Homoiconic AST, macros, direct mapping to transformations.
   • Complexity: Medium (macro system and evaluation model).

6. OCaml / F#

   • Rationale: Algebraic data types, pattern matching, strong typing.
   • Complexity: Medium (type system mapping).

7. Haskell

   • Rationale: Pure FP, strong types; great for semantic transformations.
   • Complexity: High (typeclass system, laziness).

Phase C: Concurrency/Logic Models

8. Erlang / Elixir

   • Rationale: Actor model, supervision trees; great for explicit execution annotations.
   • Complexity: High (message passing semantics).

9. Prolog

   • Rationale: Declarative semantics, unification; fits AST reasoning.
   • Complexity: High (search/backtracking model).

Phase D: Long-Tail, Specialized

10. Swift

• Rationale: ARC semantics align with annotations; Apple ecosystem.
• Complexity: High (toolchain + platform APIs).

11. Ruby / Lua

• Rationale: Dynamic languages with distinct semantics.
• Complexity: Medium.

12. Dart

• Rationale: Flutter ecosystem.
• Complexity: Medium.

WordPress Layer (Post-PHP)

• Add WordPress core API stubs.
• Introduce semantic tags for hooks, filters, actions.
• Provide guidance for safe/idiomatic WP patterns.

Notes on Projection Strategy

• Use LangSpecific annotations to preserve constructs that do not map cleanly.
• Define a minimal “semantic core” for each language and expand iteratively.
• Validate with projection matrix tests as language count grows.