graphify/graphify/interface_dispatch.py

"""Member-level dispatch from an interface requirement to its implementer(s).

A call through an injected dependency lands on the interface's method node,
because that is what the call site names: `_report.Build()` where `_report` is
an `IReport` resolves to `IReport.Build()`. The implementing method is a
separate node, and nothing joins the two, so a directed walk stops at the
interface and every chain through an injected dependency is cut at that point.

This resolver runs after all files are extracted, with the merged corpus
available, and links the interface's method to every implementing method:

    ireport_ireport_build  --dispatches_to-->  report_report_build
    ireport_ireport_build  --dispatches_to-->  alternatereport_alternatereport_build

For a static structural graph the question a call through the interface asks
is "what can this reach", not "which one does it reach at runtime" — with N
implementers the answer is all N, not none. Each implementer is still subject
to the single-owner guard `resolve_pascal_inherited_calls` and
`resolve_ruby_member_calls` already use: an implementer that itself declares
more than one method of the same name is ambiguous and is skipped on its own,
independently of its siblings.

The join is per method rather than per call site, so one edge per implementer
reconnects every call that reaches the interface method. Confidence is
`INFERRED` and falls as the candidate set grows, on the extraction rubric's own
tiers: 0.85 ("strong inference") for a single implementer — the target is
forced, the source text just never names it — 0.75 for two, and a 0.65 floor
("weak inference") from three on, rather than inventing finer-grained
precision the rubric does not have.

The mechanism knows no language. Each language registers a thin wrapper
(`graphify.csharp_dispatch` for C# interfaces, #3003) that fixes the file
suffixes both ends of a pair must be declared in: `implements` is resolved by
name, so a same-named type from another language is a collision, not an
implementation. A language whose interfaces have the same shape (Swift
protocols, for one) adds a wrapper, not a second copy of the algorithm.
"""
from __future__ import annotations

from graphify.rcfile import effective_suffix

DISPATCH_RELATION = "dispatches_to"

# Confidence falls as the implementer count grows, snapped to the extraction
# rubric's discrete INFERRED tiers (0.95/0.85/0.75/0.65/0.55) rather than a
# continuous function of count, which the rubric has no basis for (#2813
# forbids inventing precision a formula does not have).
_DISPATCH_CONFIDENCE_BY_IMPLEMENTER_COUNT = {1: 0.85, 2: 0.75}
_DISPATCH_CONFIDENCE_FLOOR = 0.65


def _dispatch_confidence(implementer_count: int) -> float:
    """INFERRED confidence for a dispatch edge, given how many implementers exist.

    One implementer is forced — "strong inference". Three or more floors at
    "weak inference" instead of continuing to fall per extra implementer.
    """
    return _DISPATCH_CONFIDENCE_BY_IMPLEMENTER_COUNT.get(implementer_count, _DISPATCH_CONFIDENCE_FLOOR)


def declared_in(node: dict | None, suffixes: tuple[str, ...]) -> bool:
    """True when the node is a declaration that lives in a file with one of ``suffixes``.

    Every end of a dispatch pair has to pass this. A sourceless stub minted for
    a dangling reference carries no members worth dispatching to, and a
    cross-language pair is a name collision rather than an implementation. The
    suffix is the effective one, so a `.graphifyrc` language declaration counts
    (#2961).
    """
    if not node:
        return False
    source_file = node.get("source_file")
    return bool(source_file) and effective_suffix(str(source_file)) in suffixes


def method_label(node: dict) -> str:
    """Return a method node's bare name, for matching.

    Case is kept: C# and Swift are case sensitive, and an implementing member
    must spell the interface member exactly, so folding case could only pair a
    declaration with a member that does not implement it.

    The name is cut at the first parenthesis rather than by stripping a trailing
    `()`. Today the extractors label every method `.Name()`, so the two are the
    same, but the pair match is keyed on this string and a label that ever carried
    a signature would silently stop matching instead of failing visibly.
    """
    label = str(node.get("label", "")).strip().removeprefix(".")
    return label.split("(", 1)[0]


def resolve_interface_dispatch(
    per_file: list[dict],
    all_nodes: list[dict],
    all_edges: list[dict],
    *,
    suffixes: tuple[str, ...],
) -> None:
    """Link each interface method to every implementation that owns it unambiguously.

    Purely additive: the existing call edge to the interface method is left in
    place, since the call site really does name the interface. ``suffixes`` is
    the language gate: the interface and each implementer must both be
    declared in a file with one of these extensions.
    """
    if not any(
        declared_in(result, suffixes)
        for result in per_file
        if isinstance(result, dict)
    ) and not any(
        declared_in(n, suffixes) for n in all_nodes
    ):
        return

    node_by_id = {n.get("id"): n for n in all_nodes}

    implementers: dict[str, set[str]] = {}
    methods_of: dict[str, dict[str, set[str]]] = {}
    for e in all_edges:
        rel = e.get("relation")
        if rel == "implements":
            implementers.setdefault(e.get("target"), set()).add(e.get("source"))
        elif rel == "method":
            owner, method_nid = e.get("source"), e.get("target")
            mnode = node_by_id.get(method_nid)
            if mnode is None or not declared_in(mnode, suffixes):
                continue
            name = method_label(mnode)
            if name:
                # A set, so the same method arriving on two `method` edges cannot
                # look like two same-named methods and trip the guard below.
                methods_of.setdefault(owner, {}).setdefault(name, set()).add(method_nid)

    if not implementers or not methods_of:
        return

    # Scoped to this relation: another edge between the two members, whatever it
    # is, says nothing about whether the dispatch link is already there.
    existing_pairs = {
        (e.get("source"), e.get("target"))
        for e in all_edges
        if e.get("relation") == DISPATCH_RELATION
    }
    new_edges: list[dict] = []

    for interface_nid, impls in implementers.items():
        interface_node = node_by_id.get(interface_nid)
        if interface_node is None or not declared_in(interface_node, suffixes):
            continue
        interface_methods = methods_of.get(interface_nid, {})
        if not interface_methods:
            continue

        # Gate first, then count: an `implements` edge from a sourceless stub
        # (a dangling/external reference) or a cross-language name collision
        # never produces an edge below, so it must not count toward "how many
        # implementers is this split between" either — that would understate
        # confidence for the implementers that actually get a dispatch edge.
        gated_impls: list[tuple[str, dict]] = []
        for impl_nid in impls:
            impl_node = node_by_id.get(impl_nid)
            if impl_node is not None and declared_in(impl_node, suffixes):
                gated_impls.append((impl_nid, impl_node))
        if not gated_impls:
            continue
        score = _dispatch_confidence(len(gated_impls))

        for impl_nid, impl_node in gated_impls:
            impl_methods = methods_of.get(impl_nid, {})
            for name, declared in interface_methods.items():
                if len(declared) != 1:
                    continue
                # Ambiguity is per implementer: one implementer overloading a
                # name does not disqualify its siblings from a clean match.
                candidates = impl_methods.get(name, set())
                if len(candidates) != 1:
                    continue
                source = next(iter(declared))
                target = next(iter(candidates))
                if source == target or (source, target) in existing_pairs:
                    continue
                existing_pairs.add((source, target))
                new_edges.append({
                    "source": source,
                    "target": target,
                    "relation": DISPATCH_RELATION,
                    "context": "call",
                    "confidence": "INFERRED",
                    "confidence_score": score,
                    "source_file": str(impl_node.get("source_file", "")),
                    "source_location": impl_node.get("source_location"),
                    "weight": 1.0,
                })

    all_edges.extend(new_edges)