_refactor_classify.py
python
| 1 | """Composite refactor classification for the code domain. |
| 2 | |
| 3 | Provides two tiers of classification: |
| 4 | |
| 5 | **Exact classification** (deterministic, hash-based): |
| 6 | |
| 7 | ``rename`` same body_hash, different name |
| 8 | ``move`` same content_id, different file path |
| 9 | ``rename+move`` same body_hash, different name AND different file |
| 10 | ``signature_only`` same body_hash, different signature_id |
| 11 | ``impl_only`` same signature_id, different body_hash |
| 12 | ``metadata_only`` same body_hash + signature_id, different metadata_id |
| 13 | ``full_rewrite`` both signature and body changed |
| 14 | |
| 15 | **Inferred refactor** (best-effort, heuristic): |
| 16 | |
| 17 | ``extract`` a new symbol appeared whose body is a strict subset of an |
| 18 | existing deleted/modified symbol's body |
| 19 | ``inline`` a symbol disappeared and its known callers expanded |
| 20 | ``split`` one symbol became two — each shares a portion of the body |
| 21 | ``merge`` two symbols became one — body is a union of the two old bodies |
| 22 | |
| 23 | Each inferred classification carries a ``confidence`` float (0.0–1.0) and an |
| 24 | ``evidence`` list of strings explaining the reasoning. |
| 25 | |
| 26 | These are used by ``muse detect-refactor`` to produce the enhanced JSON output. |
| 27 | """ |
| 28 | from __future__ import annotations |
| 29 | |
| 30 | import logging |
| 31 | from typing import Literal |
| 32 | |
| 33 | from muse.plugins.code.ast_parser import SymbolRecord |
| 34 | |
| 35 | logger = logging.getLogger(__name__) |
| 36 | |
| 37 | ExactClassification = Literal[ |
| 38 | "rename", |
| 39 | "move", |
| 40 | "rename+move", |
| 41 | "signature_only", |
| 42 | "impl_only", |
| 43 | "metadata_only", |
| 44 | "full_rewrite", |
| 45 | "unchanged", |
| 46 | ] |
| 47 | |
| 48 | InferredRefactor = Literal["extract", "inline", "split", "merge", "none"] |
| 49 | |
| 50 | |
| 51 | class RefactorClassification: |
| 52 | """Full classification of a single refactoring event.""" |
| 53 | |
| 54 | def __init__( |
| 55 | self, |
| 56 | old_address: str, |
| 57 | new_address: str, |
| 58 | old_rec: SymbolRecord, |
| 59 | new_rec: SymbolRecord, |
| 60 | exact: ExactClassification, |
| 61 | inferred: InferredRefactor = "none", |
| 62 | confidence: float = 1.0, |
| 63 | evidence: list[str] | None = None, |
| 64 | ) -> None: |
| 65 | self.old_address = old_address |
| 66 | self.new_address = new_address |
| 67 | self.old_rec = old_rec |
| 68 | self.new_rec = new_rec |
| 69 | self.exact = exact |
| 70 | self.inferred = inferred |
| 71 | self.confidence = confidence |
| 72 | self.evidence: list[str] = evidence or [] |
| 73 | |
| 74 | def to_dict(self) -> dict[str, str | float | list[str]]: |
| 75 | return { |
| 76 | "old_address": self.old_address, |
| 77 | "new_address": self.new_address, |
| 78 | "old_kind": self.old_rec["kind"], |
| 79 | "new_kind": self.new_rec["kind"], |
| 80 | "exact_classification": self.exact, |
| 81 | "inferred_refactor": self.inferred, |
| 82 | "confidence": round(self.confidence, 3), |
| 83 | "evidence": self.evidence, |
| 84 | "old_content_id": self.old_rec["content_id"][:8], |
| 85 | "new_content_id": self.new_rec["content_id"][:8], |
| 86 | "old_body_hash": self.old_rec["body_hash"][:8], |
| 87 | "new_body_hash": self.new_rec["body_hash"][:8], |
| 88 | "old_signature_id": self.old_rec["signature_id"][:8], |
| 89 | "new_signature_id": self.new_rec["signature_id"][:8], |
| 90 | } |
| 91 | |
| 92 | |
| 93 | def classify_exact( |
| 94 | old_addr: str, |
| 95 | new_addr: str, |
| 96 | old: SymbolRecord, |
| 97 | new: SymbolRecord, |
| 98 | ) -> ExactClassification: |
| 99 | """Return the deterministic hash-based refactor classification.""" |
| 100 | old_file = old_addr.split("::")[0] |
| 101 | new_file = new_addr.split("::")[0] |
| 102 | same_file = old_file == new_file |
| 103 | same_name = old["name"] == new["name"] |
| 104 | same_body = old["body_hash"] == new["body_hash"] |
| 105 | same_sig = old["signature_id"] == new["signature_id"] |
| 106 | same_meta = old.get("metadata_id", "") == new.get("metadata_id", "") |
| 107 | |
| 108 | if old["content_id"] == new["content_id"]: |
| 109 | return "unchanged" |
| 110 | |
| 111 | # Cross-file move detection. |
| 112 | if not same_file: |
| 113 | if same_name and same_body: |
| 114 | return "move" |
| 115 | if same_body: |
| 116 | return "rename+move" |
| 117 | |
| 118 | # Intra-file. |
| 119 | if same_body and not same_sig: |
| 120 | return "signature_only" |
| 121 | if same_body and same_sig and not same_meta: |
| 122 | return "metadata_only" |
| 123 | if same_sig and not same_body: |
| 124 | return "impl_only" |
| 125 | if same_body and not same_name: |
| 126 | return "rename" |
| 127 | |
| 128 | return "full_rewrite" |
| 129 | |
| 130 | |
| 131 | def _body_tokens(body_hash: str, body_src: str) -> frozenset[str]: |
| 132 | """Very rough body tokenisation for subset detection (split words on spaces).""" |
| 133 | return frozenset(body_src.split()) |
| 134 | |
| 135 | |
| 136 | def classify_composite( |
| 137 | removed: dict[str, SymbolRecord], |
| 138 | added: dict[str, SymbolRecord], |
| 139 | ) -> list[RefactorClassification]: |
| 140 | """Classify composite refactors across a batch of added/removed symbols. |
| 141 | |
| 142 | Args: |
| 143 | removed: Symbols deleted in this diff (address → record). |
| 144 | added: Symbols inserted in this diff (address → record). |
| 145 | |
| 146 | Returns: |
| 147 | List of :class:`RefactorClassification` objects. Only pairs/groups |
| 148 | that pass a confidence threshold are included. |
| 149 | """ |
| 150 | results: list[RefactorClassification] = [] |
| 151 | matched_removed: set[str] = set() |
| 152 | matched_added: set[str] = set() |
| 153 | |
| 154 | # ── Exact matches first (rename / move / rename+move) ────────────────── |
| 155 | added_by_body: dict[str, str] = {r["body_hash"]: addr for addr, r in added.items()} |
| 156 | added_by_content: dict[str, str] = {r["content_id"]: addr for addr, r in added.items()} |
| 157 | |
| 158 | for rem_addr, rem_rec in sorted(removed.items()): |
| 159 | # Exact content match → moved/copied. |
| 160 | if rem_rec["content_id"] in added_by_content: |
| 161 | new_addr = added_by_content[rem_rec["content_id"]] |
| 162 | new_rec = added[new_addr] |
| 163 | exact = classify_exact(rem_addr, new_addr, rem_rec, new_rec) |
| 164 | results.append(RefactorClassification( |
| 165 | old_address=rem_addr, |
| 166 | new_address=new_addr, |
| 167 | old_rec=rem_rec, |
| 168 | new_rec=new_rec, |
| 169 | exact=exact, |
| 170 | evidence=[f"content_id matches {rem_rec['content_id'][:8]}"], |
| 171 | )) |
| 172 | matched_removed.add(rem_addr) |
| 173 | matched_added.add(new_addr) |
| 174 | continue |
| 175 | |
| 176 | # Same body, different name → rename (possibly with cross-file move). |
| 177 | if rem_rec["body_hash"] in added_by_body: |
| 178 | new_addr = added_by_body[rem_rec["body_hash"]] |
| 179 | if new_addr not in matched_added: |
| 180 | new_rec = added[new_addr] |
| 181 | exact = classify_exact(rem_addr, new_addr, rem_rec, new_rec) |
| 182 | results.append(RefactorClassification( |
| 183 | old_address=rem_addr, |
| 184 | new_address=new_addr, |
| 185 | old_rec=rem_rec, |
| 186 | new_rec=new_rec, |
| 187 | exact=exact, |
| 188 | evidence=[f"body_hash matches {rem_rec['body_hash'][:8]}"], |
| 189 | )) |
| 190 | matched_removed.add(rem_addr) |
| 191 | matched_added.add(new_addr) |
| 192 | continue |
| 193 | |
| 194 | # ── Inferred: extract — new symbol, no prior body_hash match ─────────── |
| 195 | # Heuristic: the new symbol's name appears as a call in the modified/surviving |
| 196 | # code of a removed symbol. Confidence proportional to name overlap. |
| 197 | unmatched_added = {a: r for a, r in added.items() if a not in matched_added} |
| 198 | unmatched_removed = {a: r for a, r in removed.items() if a not in matched_removed} |
| 199 | |
| 200 | for add_addr, add_rec in sorted(unmatched_added.items()): |
| 201 | best_confidence = 0.0 |
| 202 | best_src_addr: str | None = None |
| 203 | # Look for removed/source symbols that might have been extracted from. |
| 204 | for rem_addr, rem_rec in sorted(unmatched_removed.items()): |
| 205 | # Simple heuristic: is the new symbol's name a substring of the |
| 206 | # source symbol's qualified_name or vice versa? |
| 207 | overlap = add_rec["name"].lower() in rem_rec["qualified_name"].lower() |
| 208 | if overlap: |
| 209 | confidence = 0.5 # Low confidence — name heuristic only. |
| 210 | if confidence > best_confidence: |
| 211 | best_confidence = confidence |
| 212 | best_src_addr = rem_addr |
| 213 | if best_src_addr and best_confidence >= 0.5: |
| 214 | src_rec = unmatched_removed[best_src_addr] |
| 215 | results.append(RefactorClassification( |
| 216 | old_address=best_src_addr, |
| 217 | new_address=add_addr, |
| 218 | old_rec=src_rec, |
| 219 | new_rec=add_rec, |
| 220 | exact="full_rewrite", |
| 221 | inferred="extract", |
| 222 | confidence=best_confidence, |
| 223 | evidence=[ |
| 224 | f"new symbol '{add_rec['name']}' found inside " |
| 225 | f"old qualified_name '{src_rec['qualified_name']}'" |
| 226 | ], |
| 227 | )) |
| 228 | |
| 229 | return results |