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101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Finding language-compatibility annotation.
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Classifies each finding's ``rule_id`` against known buckets so downstream
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reports can flag which findings are reliable for non-English skills.
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"""
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from __future__ import annotations
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# ---------------------------------------------------------------------------
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# Rule classification
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# ---------------------------------------------------------------------------
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# Rule IDs from LLM-based semantic analyzers — inherently multilingual.
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_SEMANTIC_RULES: frozenset[str] = frozenset(
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{
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"SSD1", "SSD2", "SSD3", "SSD4",
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"SDI1", "SDI2", "SDI3", "SDI4",
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"SQP1", "SQP2", "SQP3",
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"TP4",
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}
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)
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# Rule IDs from the gap-fill pass (P5 / P6-P8 / MP1-MP3 / RA1-RA2) —
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# these are LLM-generated for non-English skills.
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_GAP_FILL_RULES: frozenset[str] = frozenset(
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{"P5", "P6", "P7", "P8", "MP1", "MP2", "MP3", "RA1", "RA2"}
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)
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# Rule IDs from code-level analyzers — language-independent by design.
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_CODE_RULES: frozenset[str] = frozenset(
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{
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"AST1", "AST2", "AST3", "AST4", "AST5", "AST6", "AST7", "AST8",
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"TT1", "TT2", "TT3", "TT4", "TT5",
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"YR1", "YR2", "YR3", "YR4",
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"SC1", "SC2", "SC3", "SC4", "SC5", "SC6",
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"LP1", "LP2", "LP3", "LP4",
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"TP1", "TP2", "TP3",
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"TM1", "TM2", "TM3",
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}
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)
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# English-keyword static rules that have semantic-equivalent coverage
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# via SSD / SDI / SQP for non-English skills. These are listed for
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# documentation; the compatibility check treats them as needing scrutiny
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# when the detected language is non-English.
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_ENGLISH_KEYWORD_RULES: frozenset[str] = frozenset(
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{
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"P1", "P2", "P3", "P4",
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"E1", "E2", "E3", "E4",
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"PE1", "PE2", "PE3",
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"EA1", "EA2", "EA3", "EA4",
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"OH1", "OH2", "OH3",
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"TR1", "TR2", "TR3",
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}
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)
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def is_language_compatible(rule_id: str, detected_language: str) -> bool:
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"""Return ``True`` when *rule_id* is reliable for *detected_language*.
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Code-level rules are always compatible. Semantic rules are always
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compatible. English-keyword rules are only compatible when the skill
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is English. Gap-fill rules are compatible (they were generated by
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an LLM specifically for this language).
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"""
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if detected_language == "en":
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return True
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return rule_id in _SEMANTIC_RULES | _CODE_RULES | _GAP_FILL_RULES
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def annotate_findings(
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issues: list[dict[str, object]],
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detected_language: str,
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) -> list[dict[str, object]]:
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"""Add a ``language_compatible`` field to each issue dict.
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Returns a new list — the input *issues* list is not mutated.
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"""
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annotated: list[dict[str, object]] = []
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for issue in issues:
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rule_id = str(issue.get("id", ""))
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entry = dict(issue)
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entry["language_compatible"] = is_language_compatible(rule_id, detected_language)
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annotated.append(entry)
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return annotated
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