1030 lines
38 KiB
Python
1030 lines
38 KiB
Python
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"""
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Description : Semantic Analysis Layer (Layer 0)
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- RTL Structure Parser
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- Function Point Recognizer
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- Importance Scorer
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- Test Scenario Generator
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Author : CGA Enhancement Project
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Time : 2026/03/08
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"""
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import re
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import logging
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from typing import List, Dict, Tuple, Optional, Any
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from dataclasses import dataclass, field
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from enum import Enum
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logger = logging.getLogger(__name__)
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# ============================================================================
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# 数据结构定义
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# ============================================================================
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class FunctionPointType(Enum):
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"""功能点类型枚举"""
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FSM = "fsm" # 状态机
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COUNTER = "counter" # 计数器
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CONDITION = "condition" # 条件分支
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PROTOCOL = "protocol" # 协议接口
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EXCEPTION = "exception" # 异常处理
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@dataclass
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class Port:
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"""端口信息"""
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name: str
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direction: str # input, output, inout
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width: int = 1
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msb: int = 0
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lsb: int = 0
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@dataclass
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class Signal:
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"""信号信息"""
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name: str
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signal_type: str # wire, reg, logic
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width: int = 1
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msb: int = 0
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lsb: int = 0
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@dataclass
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class Parameter:
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"""参数信息"""
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name: str
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value: str
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param_type: str = "parameter" # parameter, localparam
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@dataclass
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class CodeRegion:
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"""代码区域"""
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start_line: int
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end_line: int
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region_type: str # always, assign, instance, etc.
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content: str
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@dataclass
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class FunctionPoint:
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"""功能点"""
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name: str
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fp_type: FunctionPointType
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location: Tuple[int, int] # (start_line, end_line)
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code_region: str
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importance_score: float = 0.0
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covered: bool = False
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attributes: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class TestScenario:
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"""测试场景建议"""
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name: str
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target_function: str
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description: str
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priority: float
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scenario_type: str # transition, boundary, exception, etc.
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@dataclass
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class RTLStructure:
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"""RTL结构信息"""
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module_name: str = ""
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ports: List[Port] = field(default_factory=list)
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signals: List[Signal] = field(default_factory=list)
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parameters: List[Parameter] = field(default_factory=list)
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code_regions: List[CodeRegion] = field(default_factory=list)
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signal_dependencies: Dict[str, List[str]] = field(default_factory=dict)
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# ============================================================================
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# RTL 结构解析器
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# ============================================================================
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class RTLStructureParser:
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"""
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RTL结构解析器
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解析RTL代码的基础结构信息，为后续分析提供结构化数据
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"""
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def __init__(self, rtl_code: str):
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"""
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Args:
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rtl_code: RTL源代码字符串
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"""
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self.code = rtl_code
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self.lines = rtl_code.split('\n')
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self.structure = RTLStructure()
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def parse(self) -> RTLStructure:
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"""执行完整解析"""
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self._parse_module_header()
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self._parse_ports()
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self._parse_signals()
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self._parse_parameters()
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self._parse_code_regions()
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self._build_signal_dependencies()
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return self.structure
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def _parse_module_header(self):
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"""解析模块头"""
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# 匹配 module name (...) 或 module name;
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pattern = r'module\s+(\w+)\s*(?:#\s*\([^)]*\))?\s*(?:\(([^)]*)\))?'
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match = re.search(pattern, self.code, re.DOTALL)
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if match:
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self.structure.module_name = match.group(1)
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def _parse_ports(self):
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"""解析端口定义"""
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# 从模块声明中提取端口
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port_patterns = [
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r'(input|output|inout)\s+(?:wire\s+|reg\s+)?(?:\[(\d+):(\d+)\]\s+)?(\w+)',
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r'(input|output|inout)\s+(?:\[(\d+):(\d+)\]\s+)?(\w+)',
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]
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for pattern in port_patterns:
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for match in re.finditer(pattern, self.code):
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direction = match.group(1)
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msb = int(match.group(2)) if match.group(2) else 0
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lsb = int(match.group(3)) if match.group(3) else 0
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name = match.group(4)
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width = abs(msb - lsb) + 1
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# 避免重复添加
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if not any(p.name == name for p in self.structure.ports):
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self.structure.ports.append(Port(
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name=name,
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direction=direction,
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width=width,
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msb=msb,
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lsb=lsb
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))
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def _parse_signals(self):
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"""解析内部信号定义"""
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patterns = [
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r'(wire|reg|logic)\s+(?:\[(\d+):(\d+)\]\s+)?(\w+)\s*;',
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r'(wire|reg|logic)\s+\[(\d+):(\d+)\]\s+(\w+)\s*;',
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]
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for pattern in patterns:
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for match in re.finditer(pattern, self.code):
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signal_type = match.group(1)
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msb = int(match.group(2)) if match.group(2) else 0
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lsb = int(match.group(3)) if match.group(3) else 0
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name = match.group(4)
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width = abs(msb - lsb) + 1
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if not any(s.name == name for s in self.structure.signals):
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self.structure.signals.append(Signal(
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name=name,
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signal_type=signal_type,
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width=width,
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msb=msb,
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lsb=lsb
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))
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def _parse_parameters(self):
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"""解析参数定义"""
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# parameter 定义
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param_pattern = r'parameter\s+(?:\[(\d+):(\d+)\]\s+)?(\w+)\s*=\s*([^;]+)'
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for match in re.finditer(param_pattern, self.code):
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name = match.group(3)
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value = match.group(4).strip()
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self.structure.parameters.append(Parameter(
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name=name,
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value=value,
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param_type="parameter"
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))
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# localparam 定义
|
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localparam_pattern = r'localparam\s+(?:\[(\d+):(\d+)\]\s+)?(\w+)\s*=\s*([^;]+)'
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for match in re.finditer(localparam_pattern, self.code):
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name = match.group(3)
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value = match.group(4).strip()
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self.structure.parameters.append(Parameter(
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name=name,
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value=value,
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param_type="localparam"
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))
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def _parse_code_regions(self):
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"""解析代码区域"""
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# always 块
|
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always_pattern = r'(always\s*@\s*\([^)]*\)\s*begin.*?end)'
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for i, match in enumerate(re.finditer(always_pattern, self.code, re.DOTALL)):
|
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start = self.code[:match.start()].count('\n') + 1
|
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end = self.code[:match.end()].count('\n') + 1
|
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self.structure.code_regions.append(CodeRegion(
|
|||
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start_line=start,
|
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end_line=end,
|
|||
|
|
region_type="always",
|
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|
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content=match.group(1)
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|
|
))
|
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|
|
|
|||
|
|
# assign 语句
|
|||
|
|
assign_pattern = r'(assign\s+\w+\s*=\s*[^;]+;)'
|
|||
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for i, match in enumerate(re.finditer(assign_pattern, self.code)):
|
|||
|
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start = self.code[:match.start()].count('\n') + 1
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|
end = start
|
|||
|
|
self.structure.code_regions.append(CodeRegion(
|
|||
|
|
start_line=start,
|
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end_line=end,
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|||
|
|
region_type="assign",
|
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|
|
content=match.group(1)
|
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|
|
))
|
|||
|
|
|
|||
|
|
def _build_signal_dependencies(self):
|
|||
|
|
"""构建信号依赖图"""
|
|||
|
|
# 分析 assign 语句中的依赖
|
|||
|
|
assign_pattern = r'assign\s+(\w+)\s*=\s*([^;]+);'
|
|||
|
|
for match in re.finditer(assign_pattern, self.code):
|
|||
|
|
target = match.group(1)
|
|||
|
|
source = match.group(2)
|
|||
|
|
# 提取源表达式中的信号名
|
|||
|
|
deps = re.findall(r'\b([a-zA-Z_][a-zA-Z0-9_]*)\b', source)
|
|||
|
|
self.structure.signal_dependencies[target] = deps
|
|||
|
|
|
|||
|
|
# 分析 always 块中的依赖
|
|||
|
|
always_pattern = r'always\s*@\s*\([^)]*\)\s*begin(.*?)end'
|
|||
|
|
for match in re.finditer(always_pattern, self.code, re.DOTALL):
|
|||
|
|
block = match.group(1)
|
|||
|
|
# 查找赋值语句
|
|||
|
|
assigns = re.findall(r'(\w+)\s*(?:<=|=)\s*([^;]+);', block)
|
|||
|
|
for target, source in assigns:
|
|||
|
|
deps = re.findall(r'\b([a-zA-Z_][a-zA-Z0-9_]*)\b', source)
|
|||
|
|
if target not in self.structure.signal_dependencies:
|
|||
|
|
self.structure.signal_dependencies[target] = []
|
|||
|
|
self.structure.signal_dependencies[target].extend(deps)
|
|||
|
|
|
|||
|
|
def get_input_ports(self) -> List[str]:
|
|||
|
|
"""获取输入端口名列表"""
|
|||
|
|
return [p.name for p in self.structure.ports if p.direction == 'input']
|
|||
|
|
|
|||
|
|
def get_output_ports(self) -> List[str]:
|
|||
|
|
"""获取输出端口名列表"""
|
|||
|
|
return [p.name for p in self.structure.ports if p.direction == 'output']
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ============================================================================
|
|||
|
|
# 功能点识别器
|
|||
|
|
# ============================================================================
|
|||
|
|
|
|||
|
|
class FunctionPointRecognizer:
|
|||
|
|
"""
|
|||
|
|
功能点识别器
|
|||
|
|
自动识别RTL代码中的关键功能点
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
def __init__(self, rtl_code: str, structure: RTLStructure):
|
|||
|
|
"""
|
|||
|
|
Args:
|
|||
|
|
rtl_code: RTL源代码
|
|||
|
|
structure: 已解析的结构信息
|
|||
|
|
"""
|
|||
|
|
self.code = rtl_code
|
|||
|
|
self.structure = structure
|
|||
|
|
self.function_points: List[FunctionPoint] = []
|
|||
|
|
|
|||
|
|
def recognize(self) -> List[FunctionPoint]:
|
|||
|
|
"""执行完整识别"""
|
|||
|
|
self._recognize_fsm()
|
|||
|
|
self._recognize_counters()
|
|||
|
|
self._recognize_conditions()
|
|||
|
|
self._recognize_protocols()
|
|||
|
|
self._recognize_exceptions()
|
|||
|
|
return self.function_points
|
|||
|
|
|
|||
|
|
def _recognize_fsm(self):
|
|||
|
|
"""识别状态机"""
|
|||
|
|
# 查找状态参数定义
|
|||
|
|
state_params = []
|
|||
|
|
param_pattern = r'(?:parameter|localparam)\s+([^;]+);'
|
|||
|
|
for match in re.finditer(param_pattern, self.code):
|
|||
|
|
params_str = match.group(1)
|
|||
|
|
# 解析状态定义，如 WL=0, WR=1, ...
|
|||
|
|
state_defs = re.findall(r'([A-Z][A-Z0-9_]*)\s*=\s*(\d+)', params_str)
|
|||
|
|
if state_defs:
|
|||
|
|
state_params.extend([s[0] for s in state_defs])
|
|||
|
|
|
|||
|
|
if not state_params:
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
# 查找 case 语句（状态转换逻辑）
|
|||
|
|
case_pattern = r'case\s*\(\s*(\w+)\s*\)(.*?)(?:endcase|$)'
|
|||
|
|
for match in re.finditer(case_pattern, self.code, re.DOTALL):
|
|||
|
|
state_var = match.group(1)
|
|||
|
|
case_body = match.group(2)
|
|||
|
|
|
|||
|
|
# 提取状态转换信息
|
|||
|
|
transitions = self._extract_fsm_transitions(case_body, state_params)
|
|||
|
|
|
|||
|
|
start_line = self.code[:match.start()].count('\n') + 1
|
|||
|
|
end_line = self.code[:match.end()].count('\n') + 1
|
|||
|
|
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name=f"FSM_{state_var}",
|
|||
|
|
fp_type=FunctionPointType.FSM,
|
|||
|
|
location=(start_line, end_line),
|
|||
|
|
code_region=match.group(0),
|
|||
|
|
attributes={
|
|||
|
|
'state_variable': state_var,
|
|||
|
|
'states': state_params,
|
|||
|
|
'transitions': transitions
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
def _extract_fsm_transitions(self, case_body: str, states: List[str]) -> Dict[str, List[Dict]]:
|
|||
|
|
"""提取 FSM 状态转换信息"""
|
|||
|
|
transitions = {}
|
|||
|
|
|
|||
|
|
# 匹配每个状态分支
|
|||
|
|
branch_pattern = r'([A-Z][A-Z0-9_]*)\s*:(.*?)(?=[A-Z][A-Z0-9_]*\s*:|endcase|default:)'
|
|||
|
|
for match in re.finditer(branch_pattern, case_body, re.DOTALL):
|
|||
|
|
current_state = match.group(1)
|
|||
|
|
branch_code = match.group(2)
|
|||
|
|
|
|||
|
|
if current_state not in states:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
transitions[current_state] = []
|
|||
|
|
|
|||
|
|
# 提取条件转换
|
|||
|
|
cond_pattern = r'(?:if|else\s*if)\s*\(([^)]+)\)\s*(?:next\s*=?|state\s*<=)\s*(\w+)'
|
|||
|
|
for cond_match in re.finditer(cond_pattern, branch_code):
|
|||
|
|
condition = cond_match.group(1).strip()
|
|||
|
|
next_state = cond_match.group(2).strip()
|
|||
|
|
transitions[current_state].append({
|
|||
|
|
'condition': condition,
|
|||
|
|
'next_state': next_state
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# 提取 default 转换
|
|||
|
|
default_pattern = r'else\s*(?:next\s*=?|state\s*<=)\s*(\w+)'
|
|||
|
|
default_match = re.search(default_pattern, branch_code)
|
|||
|
|
if default_match:
|
|||
|
|
transitions[current_state].append({
|
|||
|
|
'condition': 'default',
|
|||
|
|
'next_state': default_match.group(1)
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return transitions
|
|||
|
|
|
|||
|
|
def _recognize_counters(self):
|
|||
|
|
"""识别计数器"""
|
|||
|
|
# 查找自增/自减操作
|
|||
|
|
counter_patterns = [
|
|||
|
|
r'(\w+)\s*<=\s*\1\s*\+\s*1', # counter <= counter + 1
|
|||
|
|
r'(\w+)\s*<=\s*\1\s*-\s*1', # counter <= counter - 1
|
|||
|
|
r'(\w+)\s*=\s*\1\s*\+\s*1', # counter = counter + 1
|
|||
|
|
r'(\w+)\s*=\s*\1\s*-\s*1', # counter = counter - 1
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
counter_candidates = set()
|
|||
|
|
for pattern in counter_patterns:
|
|||
|
|
for match in re.finditer(pattern, self.code):
|
|||
|
|
counter_candidates.add(match.group(1))
|
|||
|
|
|
|||
|
|
for counter_name in counter_candidates:
|
|||
|
|
# 查找计数器的边界条件
|
|||
|
|
boundary_pattern = rf'{counter_name}\s*(>=|<=|==|>|<)\s*(\d+)'
|
|||
|
|
boundaries = []
|
|||
|
|
for match in re.finditer(boundary_pattern, self.code):
|
|||
|
|
op = match.group(1)
|
|||
|
|
value = int(match.group(2))
|
|||
|
|
boundaries.append({'operator': op, 'value': value})
|
|||
|
|
|
|||
|
|
# 查找计数器位宽
|
|||
|
|
width = 1
|
|||
|
|
for signal in self.structure.signals:
|
|||
|
|
if signal.name == counter_name:
|
|||
|
|
width = signal.width
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# 查找计数器位置
|
|||
|
|
pattern = rf'{counter_name}\s*<='
|
|||
|
|
match = re.search(pattern, self.code)
|
|||
|
|
if match:
|
|||
|
|
start_line = self.code[:match.start()].count('\n') + 1
|
|||
|
|
end_line = start_line
|
|||
|
|
else:
|
|||
|
|
start_line, end_line = 0, 0
|
|||
|
|
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name=f"Counter_{counter_name}",
|
|||
|
|
fp_type=FunctionPointType.COUNTER,
|
|||
|
|
location=(start_line, end_line),
|
|||
|
|
code_region="",
|
|||
|
|
attributes={
|
|||
|
|
'counter_name': counter_name,
|
|||
|
|
'width': width,
|
|||
|
|
'boundaries': boundaries
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
def _recognize_conditions(self):
|
|||
|
|
"""识别条件分支"""
|
|||
|
|
# 查找 if-else 嵌套
|
|||
|
|
if_pattern = r'(if\s*\([^)]+\)(?:\s*begin)?(?:[^;]*?)(?:end)?(?:\s*else\s*(?:if\s*\([^)]+\))?(?:\s*begin)?(?:[^;]*?)(?:end)?)*)'
|
|||
|
|
|
|||
|
|
# 简化：查找独立的 if 语句
|
|||
|
|
simple_if_pattern = r'if\s*\(([^)]+)\)'
|
|||
|
|
for i, match in enumerate(re.finditer(simple_if_pattern, self.code)):
|
|||
|
|
condition = match.group(1).strip()
|
|||
|
|
|
|||
|
|
start_line = self.code[:match.start()].count('\n') + 1
|
|||
|
|
end_line = start_line
|
|||
|
|
|
|||
|
|
# 分析条件中的边界值
|
|||
|
|
boundary_values = re.findall(r'(>=|<=|==|>|<)\s*(\d+)', condition)
|
|||
|
|
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name=f"Condition_{i+1}",
|
|||
|
|
fp_type=FunctionPointType.CONDITION,
|
|||
|
|
location=(start_line, end_line),
|
|||
|
|
code_region=match.group(0),
|
|||
|
|
attributes={
|
|||
|
|
'condition': condition,
|
|||
|
|
'boundary_values': boundary_values
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
def _recognize_protocols(self):
|
|||
|
|
"""识别协议接口"""
|
|||
|
|
# 查找握手信号
|
|||
|
|
handshake_patterns = [
|
|||
|
|
(r'\b(valid|vld)\b', 'valid'),
|
|||
|
|
(r'\b(ready|rdy)\b', 'ready'),
|
|||
|
|
(r'\b(ack|acknowledge)\b', 'ack'),
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
handshake_signals = {}
|
|||
|
|
for pattern, signal_type in handshake_patterns:
|
|||
|
|
for match in re.finditer(pattern, self.code, re.IGNORECASE):
|
|||
|
|
signal_name = match.group(1)
|
|||
|
|
if signal_type not in handshake_signals:
|
|||
|
|
handshake_signals[signal_type] = []
|
|||
|
|
handshake_signals[signal_type].append(signal_name)
|
|||
|
|
|
|||
|
|
if len(handshake_signals) >= 2:
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name="Protocol_Handshake",
|
|||
|
|
fp_type=FunctionPointType.PROTOCOL,
|
|||
|
|
location=(0, 0),
|
|||
|
|
code_region="",
|
|||
|
|
attributes={
|
|||
|
|
'protocol_type': 'handshake',
|
|||
|
|
'signals': handshake_signals
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
def _recognize_exceptions(self):
|
|||
|
|
"""识别异常处理"""
|
|||
|
|
# 查找 error 信号
|
|||
|
|
error_pattern = r'(?:output|wire)\s+(?:\[[\d:]+\]\s+)?(\w*error\w*)'
|
|||
|
|
for match in re.finditer(error_pattern, self.code, re.IGNORECASE):
|
|||
|
|
error_signal = match.group(1)
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name=f"Exception_{error_signal}",
|
|||
|
|
fp_type=FunctionPointType.EXCEPTION,
|
|||
|
|
location=(0, 0),
|
|||
|
|
code_region="",
|
|||
|
|
attributes={
|
|||
|
|
'error_signal': error_signal
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
# 查找 default 分支（case 语句的异常处理）
|
|||
|
|
default_pattern = r'default\s*:'
|
|||
|
|
if re.search(default_pattern, self.code):
|
|||
|
|
fp = FunctionPoint(
|
|||
|
|
name="Exception_DefaultCase",
|
|||
|
|
fp_type=FunctionPointType.EXCEPTION,
|
|||
|
|
location=(0, 0),
|
|||
|
|
code_region="",
|
|||
|
|
attributes={
|
|||
|
|
'type': 'default_case'
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
self.function_points.append(fp)
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ============================================================================
|
|||
|
|
# 重要性评分器
|
|||
|
|
# ============================================================================
|
|||
|
|
|
|||
|
|
class ImportanceScorer:
|
|||
|
|
"""
|
|||
|
|
重要性评分器
|
|||
|
|
为每个功能点计算语义重要性评分
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
# 类型权重
|
|||
|
|
TYPE_WEIGHTS = {
|
|||
|
|
FunctionPointType.FSM: 0.30,
|
|||
|
|
FunctionPointType.COUNTER: 0.20,
|
|||
|
|
FunctionPointType.PROTOCOL: 0.20,
|
|||
|
|
FunctionPointType.CONDITION: 0.15,
|
|||
|
|
FunctionPointType.EXCEPTION: 0.15,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def __init__(self, function_points: List[FunctionPoint], structure: RTLStructure):
|
|||
|
|
"""
|
|||
|
|
Args:
|
|||
|
|
function_points: 功能点列表
|
|||
|
|
structure: RTL结构信息
|
|||
|
|
"""
|
|||
|
|
self.function_points = function_points
|
|||
|
|
self.structure = structure
|
|||
|
|
|
|||
|
|
def score(self) -> List[FunctionPoint]:
|
|||
|
|
"""计算所有功能点的重要性评分"""
|
|||
|
|
for fp in self.function_points:
|
|||
|
|
fp.importance_score = self._calculate_score(fp)
|
|||
|
|
|
|||
|
|
# 按评分排序
|
|||
|
|
self.function_points.sort(key=lambda x: x.importance_score, reverse=True)
|
|||
|
|
return self.function_points
|
|||
|
|
|
|||
|
|
def _calculate_score(self, fp: FunctionPoint) -> float:
|
|||
|
|
"""计算单个功能点的评分"""
|
|||
|
|
# 基础类型权重
|
|||
|
|
base_score = self.TYPE_WEIGHTS.get(fp.fp_type, 0.10)
|
|||
|
|
|
|||
|
|
# 边界条件得分
|
|||
|
|
boundary_score = self._get_boundary_score(fp)
|
|||
|
|
|
|||
|
|
# 异常路径得分
|
|||
|
|
exception_score = self._get_exception_score(fp)
|
|||
|
|
|
|||
|
|
# 依赖深度得分
|
|||
|
|
dependency_score = self._get_dependency_score(fp)
|
|||
|
|
|
|||
|
|
# 复杂度得分
|
|||
|
|
complexity_score = self._get_complexity_score(fp)
|
|||
|
|
|
|||
|
|
# 综合评分
|
|||
|
|
total_score = (
|
|||
|
|
base_score +
|
|||
|
|
0.30 * boundary_score +
|
|||
|
|
0.25 * exception_score +
|
|||
|
|
0.20 * dependency_score +
|
|||
|
|
0.25 * complexity_score
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return min(1.0, total_score) # 归一化到 [0, 1]
|
|||
|
|
|
|||
|
|
def _get_boundary_score(self, fp: FunctionPoint) -> float:
|
|||
|
|
"""获取边界条件得分"""
|
|||
|
|
if fp.fp_type == FunctionPointType.COUNTER:
|
|||
|
|
boundaries = fp.attributes.get('boundaries', [])
|
|||
|
|
if boundaries:
|
|||
|
|
# 有边界值的计数器更重要
|
|||
|
|
return min(1.0, len(boundaries) * 0.3)
|
|||
|
|
|
|||
|
|
if fp.fp_type == FunctionPointType.CONDITION:
|
|||
|
|
boundary_values = fp.attributes.get('boundary_values', [])
|
|||
|
|
if boundary_values:
|
|||
|
|
return min(1.0, len(boundary_values) * 0.2)
|
|||
|
|
|
|||
|
|
if fp.fp_type == FunctionPointType.FSM:
|
|||
|
|
states = fp.attributes.get('states', [])
|
|||
|
|
# 状态越多，边界条件越重要
|
|||
|
|
return min(1.0, len(states) * 0.1)
|
|||
|
|
|
|||
|
|
return 0.0
|
|||
|
|
|
|||
|
|
def _get_exception_score(self, fp: FunctionPoint) -> float:
|
|||
|
|
"""获取异常路径得分"""
|
|||
|
|
if fp.fp_type == FunctionPointType.EXCEPTION:
|
|||
|
|
return 1.0
|
|||
|
|
|
|||
|
|
if fp.fp_type == FunctionPointType.FSM:
|
|||
|
|
transitions = fp.attributes.get('transitions', {})
|
|||
|
|
# 检查是否有异常状态转换
|
|||
|
|
for state, trans_list in transitions.items():
|
|||
|
|
for trans in trans_list:
|
|||
|
|
if 'error' in trans.get('next_state', '').lower():
|
|||
|
|
return 0.5
|
|||
|
|
|
|||
|
|
return 0.0
|
|||
|
|
|
|||
|
|
def _get_dependency_score(self, fp: FunctionPoint) -> float:
|
|||
|
|
"""获取依赖深度得分"""
|
|||
|
|
# 分析功能点在信号依赖图中的深度
|
|||
|
|
if fp.fp_type == FunctionPointType.FSM:
|
|||
|
|
state_var = fp.attributes.get('state_variable', '')
|
|||
|
|
if state_var in self.structure.signal_dependencies:
|
|||
|
|
depth = self._calculate_dependency_depth(state_var, set())
|
|||
|
|
return min(1.0, depth * 0.1)
|
|||
|
|
|
|||
|
|
return 0.0
|
|||
|
|
|
|||
|
|
def _calculate_dependency_depth(self, signal: str, visited: set) -> int:
|
|||
|
|
"""递归计算依赖深度"""
|
|||
|
|
if signal in visited:
|
|||
|
|
return 0
|
|||
|
|
visited.add(signal)
|
|||
|
|
|
|||
|
|
deps = self.structure.signal_dependencies.get(signal, [])
|
|||
|
|
if not deps:
|
|||
|
|
return 0
|
|||
|
|
|
|||
|
|
max_depth = 0
|
|||
|
|
for dep in deps:
|
|||
|
|
depth = self._calculate_dependency_depth(dep, visited)
|
|||
|
|
max_depth = max(max_depth, depth)
|
|||
|
|
|
|||
|
|
return max_depth + 1
|
|||
|
|
|
|||
|
|
def _get_complexity_score(self, fp: FunctionPoint) -> float:
|
|||
|
|
"""获取复杂度得分"""
|
|||
|
|
if fp.fp_type == FunctionPointType.FSM:
|
|||
|
|
transitions = fp.attributes.get('transitions', {})
|
|||
|
|
# 状态转换越多，复杂度越高
|
|||
|
|
total_transitions = sum(len(t) for t in transitions.values())
|
|||
|
|
return min(1.0, total_transitions * 0.05)
|
|||
|
|
|
|||
|
|
if fp.fp_type == FunctionPointType.CONDITION:
|
|||
|
|
condition = fp.attributes.get('condition', '')
|
|||
|
|
# 条件越复杂（包含的逻辑运算符越多），复杂度越高
|
|||
|
|
operators = len(re.findall(r'(&&|\|\|)', condition))
|
|||
|
|
return min(1.0, operators * 0.2)
|
|||
|
|
|
|||
|
|
return 0.0
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ============================================================================
|
|||
|
|
# 测试场景生成器
|
|||
|
|
# ============================================================================
|
|||
|
|
|
|||
|
|
class TestScenarioGenerator:
|
|||
|
|
"""
|
|||
|
|
测试场景生成器
|
|||
|
|
基于识别的功能点生成测试场景建议
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
SCENARIO_TEMPLATES = {
|
|||
|
|
FunctionPointType.FSM: [
|
|||
|
|
("state_transition", "测试所有状态转换路径"),
|
|||
|
|
("illegal_transition", "测试非法状态转换"),
|
|||
|
|
("state_loop", "测试状态循环"),
|
|||
|
|
],
|
|||
|
|
FunctionPointType.COUNTER: [
|
|||
|
|
("boundary_zero", "测试计数器归零"),
|
|||
|
|
("boundary_max", "测试计数器最大值"),
|
|||
|
|
("overflow", "测试计数器溢出"),
|
|||
|
|
],
|
|||
|
|
FunctionPointType.CONDITION: [
|
|||
|
|
("branch_path", "测试各分支路径"),
|
|||
|
|
("boundary_condition", "测试边界条件"),
|
|||
|
|
("combination", "测试组合条件"),
|
|||
|
|
],
|
|||
|
|
FunctionPointType.PROTOCOL: [
|
|||
|
|
("handshake_timing", "测试握手时序"),
|
|||
|
|
("concurrent", "测试并发操作"),
|
|||
|
|
("timeout", "测试协议超时"),
|
|||
|
|
],
|
|||
|
|
FunctionPointType.EXCEPTION: [
|
|||
|
|
("trigger", "测试异常触发"),
|
|||
|
|
("recovery", "测试异常恢复"),
|
|||
|
|
("fault_tolerance", "测试容错处理"),
|
|||
|
|
],
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def __init__(self, function_points: List[FunctionPoint]):
|
|||
|
|
"""
|
|||
|
|
Args:
|
|||
|
|
function_points: 带评分的功能点列表
|
|||
|
|
"""
|
|||
|
|
self.function_points = function_points
|
|||
|
|
|
|||
|
|
def generate(self) -> List[TestScenario]:
|
|||
|
|
"""生成测试场景建议"""
|
|||
|
|
scenarios = []
|
|||
|
|
|
|||
|
|
for fp in self.function_points:
|
|||
|
|
templates = self.SCENARIO_TEMPLATES.get(fp.fp_type, [])
|
|||
|
|
for template_name, description in templates:
|
|||
|
|
# 根据重要性评分计算优先级
|
|||
|
|
priority = fp.importance_score
|
|||
|
|
|
|||
|
|
# 生成具体的场景描述
|
|||
|
|
specific_desc = self._customize_description(fp, template_name, description)
|
|||
|
|
|
|||
|
|
scenario = TestScenario(
|
|||
|
|
name=f"{fp.name}_{template_name}",
|
|||
|
|
target_function=fp.name,
|
|||
|
|
description=specific_desc,
|
|||
|
|
priority=priority,
|
|||
|
|
scenario_type=template_name
|
|||
|
|
)
|
|||
|
|
scenarios.append(scenario)
|
|||
|
|
|
|||
|
|
# 按优先级排序
|
|||
|
|
scenarios.sort(key=lambda x: x.priority, reverse=True)
|
|||
|
|
return scenarios
|
|||
|
|
|
|||
|
|
def _customize_description(self, fp: FunctionPoint, template_name: str, base_desc: str) -> str:
|
|||
|
|
"""根据功能点属性定制描述"""
|
|||
|
|
if fp.fp_type == FunctionPointType.FSM:
|
|||
|
|
states = fp.attributes.get('states', [])
|
|||
|
|
transitions = fp.attributes.get('transitions', {})
|
|||
|
|
|
|||
|
|
if template_name == "state_transition":
|
|||
|
|
return f"测试 {fp.name} 的所有状态转换（共 {len(states)} 个状态，{sum(len(t) for t in transitions.values())} 个转换）"
|
|||
|
|
elif template_name == "illegal_transition":
|
|||
|
|
return f"测试 {fp.name} 的非法状态转换处理"
|
|||
|
|
elif template_name == "state_loop":
|
|||
|
|
return f"测试 {fp.name} 的状态循环场景"
|
|||
|
|
|
|||
|
|
elif fp.fp_type == FunctionPointType.COUNTER:
|
|||
|
|
counter_name = fp.attributes.get('counter_name', '')
|
|||
|
|
boundaries = fp.attributes.get('boundaries', [])
|
|||
|
|
|
|||
|
|
if template_name == "boundary_zero":
|
|||
|
|
return f"测试计数器 {counter_name} 归零场景"
|
|||
|
|
elif template_name == "boundary_max":
|
|||
|
|
max_boundary = next((b for b in boundaries if b['operator'] in ['>=', '>']), None)
|
|||
|
|
if max_boundary:
|
|||
|
|
return f"测试计数器 {counter_name} 达到最大值 {max_boundary['value']}"
|
|||
|
|
return f"测试计数器 {counter_name} 最大值边界"
|
|||
|
|
elif template_name == "overflow":
|
|||
|
|
return f"测试计数器 {counter_name} 溢出场景"
|
|||
|
|
|
|||
|
|
elif fp.fp_type == FunctionPointType.CONDITION:
|
|||
|
|
condition = fp.attributes.get('condition', '')
|
|||
|
|
|
|||
|
|
if template_name == "branch_path":
|
|||
|
|
return f"测试条件分支: {condition}"
|
|||
|
|
elif template_name == "boundary_condition":
|
|||
|
|
boundary_values = fp.attributes.get('boundary_values', [])
|
|||
|
|
if boundary_values:
|
|||
|
|
values_str = ', '.join([f"{b[0]}{b[1]}" for b in boundary_values])
|
|||
|
|
return f"测试边界条件: {values_str}"
|
|||
|
|
return f"测试条件边界: {condition}"
|
|||
|
|
|
|||
|
|
return base_desc
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ============================================================================
|
|||
|
|
# 语义分析器 (主入口)
|
|||
|
|
# ============================================================================
|
|||
|
|
|
|||
|
|
class SemanticAnalyzer:
|
|||
|
|
"""
|
|||
|
|
语义分析器 - 第0层主入口
|
|||
|
|
整合所有子模块，提供统一的语义分析接口
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
def __init__(self, rtl_code: str):
|
|||
|
|
"""
|
|||
|
|
Args:
|
|||
|
|
rtl_code: RTL源代码字符串
|
|||
|
|
"""
|
|||
|
|
self.rtl_code = rtl_code
|
|||
|
|
self.structure: Optional[RTLStructure] = None
|
|||
|
|
self.function_points: List[FunctionPoint] = []
|
|||
|
|
self.test_scenarios: List[TestScenario] = []
|
|||
|
|
|
|||
|
|
def analyze(self) -> Dict[str, Any]:
|
|||
|
|
"""
|
|||
|
|
执行完整的语义分析
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
包含所有分析结果的字典
|
|||
|
|
"""
|
|||
|
|
# Step 1: 解析RTL结构
|
|||
|
|
parser = RTLStructureParser(self.rtl_code)
|
|||
|
|
self.structure = parser.parse()
|
|||
|
|
|
|||
|
|
# Step 2: 识别功能点
|
|||
|
|
recognizer = FunctionPointRecognizer(self.rtl_code, self.structure)
|
|||
|
|
self.function_points = recognizer.recognize()
|
|||
|
|
|
|||
|
|
# Step 3: 计算重要性评分
|
|||
|
|
scorer = ImportanceScorer(self.function_points, self.structure)
|
|||
|
|
self.function_points = scorer.score()
|
|||
|
|
|
|||
|
|
# Step 4: 生成测试场景
|
|||
|
|
generator = TestScenarioGenerator(self.function_points)
|
|||
|
|
self.test_scenarios = generator.generate()
|
|||
|
|
|
|||
|
|
return self.get_analysis_result()
|
|||
|
|
|
|||
|
|
# def get_analysis_result(self) -> Dict[str, Any]:
|
|||
|
|
# """获取分析结果"""
|
|||
|
|
# return {
|
|||
|
|
# 'module_name': self.structure.module_name if self.structure else '',
|
|||
|
|
# 'inputs': self.structure.get_input_ports() if self.structure else [],
|
|||
|
|
# 'outputs': self.structure.get_output_ports() if self.structure else [],
|
|||
|
|
# 'function_points': [
|
|||
|
|
# {
|
|||
|
|
# 'name': fp.name,
|
|||
|
|
# 'type': fp.fp_type.value,
|
|||
|
|
# 'importance': fp.importance_score,
|
|||
|
|
# 'covered': fp.covered,
|
|||
|
|
# 'attributes': fp.attributes
|
|||
|
|
# }
|
|||
|
|
# for fp in self.function_points
|
|||
|
|
# ],
|
|||
|
|
# 'test_scenarios': [
|
|||
|
|
# {
|
|||
|
|
# 'name': ts.name,
|
|||
|
|
# 'target': ts.target_function,
|
|||
|
|
# 'description': ts.description,
|
|||
|
|
# 'priority': ts.priority
|
|||
|
|
# }
|
|||
|
|
# for ts in self.test_scenarios
|
|||
|
|
# ]
|
|||
|
|
# }
|
|||
|
|
|
|||
|
|
def get_analysis_result(self) -> Dict[str, Any]:
|
|||
|
|
"""获取分析结果"""
|
|||
|
|
# 从 structure.ports 中获取输入输出端口
|
|||
|
|
inputs = [p.name for p in self.structure.ports if p.direction == 'input'] if self.structure else []
|
|||
|
|
outputs = [p.name for p in self.structure.ports if p.direction == 'output'] if self.structure else []
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
'module_name': self.structure.module_name if self.structure else '',
|
|||
|
|
'inputs': inputs,
|
|||
|
|
'outputs': outputs,
|
|||
|
|
'ports': [
|
|||
|
|
{
|
|||
|
|
'name': port.name,
|
|||
|
|
'direction': port.direction,
|
|||
|
|
'width': port.width,
|
|||
|
|
'msb': port.msb,
|
|||
|
|
'lsb': port.lsb
|
|||
|
|
}
|
|||
|
|
for port in self.structure.ports
|
|||
|
|
] if self.structure else [],
|
|||
|
|
'function_points': [
|
|||
|
|
{
|
|||
|
|
'name': fp.name,
|
|||
|
|
'type': fp.fp_type.value,
|
|||
|
|
'importance': fp.importance_score,
|
|||
|
|
'covered': fp.covered,
|
|||
|
|
'location': {
|
|||
|
|
'start_line': fp.location[0],
|
|||
|
|
'end_line': fp.location[1]
|
|||
|
|
},
|
|||
|
|
'attributes': fp.attributes
|
|||
|
|
}
|
|||
|
|
for fp in self.function_points
|
|||
|
|
],
|
|||
|
|
'test_scenarios': [
|
|||
|
|
{
|
|||
|
|
'name': ts.name,
|
|||
|
|
'target': ts.target_function,
|
|||
|
|
'description': ts.description,
|
|||
|
|
'priority': ts.priority
|
|||
|
|
}
|
|||
|
|
for ts in self.test_scenarios
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def get_function_points_by_type(self, fp_type: FunctionPointType) -> List[FunctionPoint]:
|
|||
|
|
"""按类型获取功能点"""
|
|||
|
|
return [fp for fp in self.function_points if fp.fp_type == fp_type]
|
|||
|
|
|
|||
|
|
def get_top_priority_scenarios(self, n: int = 5) -> List[TestScenario]:
|
|||
|
|
"""获取优先级最高的N个测试场景"""
|
|||
|
|
return self.test_scenarios[:n]
|
|||
|
|
|
|||
|
|
def get_fsm_info(self) -> Optional[Dict[str, Any]]:
|
|||
|
|
"""获取FSM相关信息（如果有）"""
|
|||
|
|
fsm_fps = self.get_function_points_by_type(FunctionPointType.FSM)
|
|||
|
|
if fsm_fps:
|
|||
|
|
fp = fsm_fps[0]
|
|||
|
|
return {
|
|||
|
|
'name': fp.name,
|
|||
|
|
'state_variable': fp.attributes.get('state_variable', ''),
|
|||
|
|
'states': fp.attributes.get('states', []),
|
|||
|
|
'transitions': fp.attributes.get('transitions', {})
|
|||
|
|
}
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
# def generate_prompt_context(self) -> str:
|
|||
|
|
# """生成用于 Prompt 的上下文信息"""
|
|||
|
|
# context = []
|
|||
|
|
|
|||
|
|
# # 端口信息
|
|||
|
|
# if self.structure:
|
|||
|
|
# context.append("[MODULE INFO]")
|
|||
|
|
# context.append(f"Module: {self.structure.module_name}")
|
|||
|
|
# # context.append(f"Inputs: {', '.join(self.structure.get_input_ports())}")
|
|||
|
|
# context.append(f"inputs = [p.name for p in self.structure.ports if p.direction == 'input']")
|
|||
|
|
# context.append(f"Outputs: {', '.join(self.structure.get_output_ports())}")
|
|||
|
|
# context.append("")
|
|||
|
|
|
|||
|
|
# # 功能点信息
|
|||
|
|
# if self.function_points:
|
|||
|
|
# context.append("[FUNCTION POINTS - Ranked by Importance]")
|
|||
|
|
# for i, fp in enumerate(self.function_points[:10]): # Top 10
|
|||
|
|
# context.append(f"{i+1}. {fp.name} ({fp.fp_type.value}): importance={fp.importance_score:.2f}")
|
|||
|
|
# context.append("")
|
|||
|
|
|
|||
|
|
# # FSM 详细信息
|
|||
|
|
# fsm_info = self.get_fsm_info()
|
|||
|
|
# if fsm_info:
|
|||
|
|
# context.append("[FSM DETAILS]")
|
|||
|
|
# context.append(f"State Variable: {fsm_info['state_variable']}")
|
|||
|
|
# context.append(f"States: {', '.join(fsm_info['states'])}")
|
|||
|
|
# context.append("Transitions:")
|
|||
|
|
# for state, trans_list in fsm_info['transitions'].items():
|
|||
|
|
# for trans in trans_list:
|
|||
|
|
# context.append(f" {state} --[{trans['condition']}]--> {trans['next_state']}")
|
|||
|
|
# context.append("")
|
|||
|
|
|
|||
|
|
# # 测试场景建议
|
|||
|
|
# if self.test_scenarios:
|
|||
|
|
# context.append("[RECOMMENDED TEST SCENARIOS]")
|
|||
|
|
# for i, ts in enumerate(self.test_scenarios[:5]):
|
|||
|
|
# context.append(f"{i+1}. {ts.name}: {ts.description} (priority={ts.priority:.2f})")
|
|||
|
|
|
|||
|
|
# return "\n".join(context)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def generate_prompt_context(self) -> str:
|
|||
|
|
"""生成用于 Prompt 的上下文信息"""
|
|||
|
|
context = []
|
|||
|
|
|
|||
|
|
# 端口信息
|
|||
|
|
if self.structure:
|
|||
|
|
inputs = [p.name for p in self.structure.ports if p.direction == 'input']
|
|||
|
|
outputs = [p.name for p in self.structure.ports if p.direction == 'output']
|
|||
|
|
context.append("[MODULE INFO]")
|
|||
|
|
context.append(f"Module: {self.structure.module_name}")
|
|||
|
|
context.append(f"Inputs: {', '.join(inputs)}")
|
|||
|
|
context.append(f"Outputs: {', '.join(outputs)}")
|
|||
|
|
context.append("")
|
|||
|
|
|
|||
|
|
# 功能点信息
|
|||
|
|
if self.function_points:
|
|||
|
|
context.append("[FUNCTION POINTS - Ranked by Importance]")
|
|||
|
|
for i, fp in enumerate(self.function_points[:10]): # Top 10
|
|||
|
|
context.append(f"{i+1}. {fp.name} ({fp.fp_type.value}): importance={fp.importance_score:.2f}")
|
|||
|
|
context.append("")
|
|||
|
|
|
|||
|
|
# FSM 详细信息
|
|||
|
|
fsm_info = self.get_fsm_info()
|
|||
|
|
if fsm_info:
|
|||
|
|
context.append("[FSM DETAILS]")
|
|||
|
|
context.append(f"State Variable: {fsm_info['state_variable']}")
|
|||
|
|
context.append(f"States: {', '.join(fsm_info['states'])}")
|
|||
|
|
context.append("Transitions:")
|
|||
|
|
for state, trans_list in fsm_info['transitions'].items():
|
|||
|
|
for trans in trans_list:
|
|||
|
|
context.append(f" {state} --[{trans['condition']}]--> {trans['next_state']}")
|
|||
|
|
context.append("")
|
|||
|
|
|
|||
|
|
# 测试场景建议
|
|||
|
|
if self.test_scenarios:
|
|||
|
|
context.append("[RECOMMENDED TEST SCENARIOS]")
|
|||
|
|
for i, ts in enumerate(self.test_scenarios[:5]):
|
|||
|
|
context.append(f"{i+1}. {ts.name}: {ts.description} (priority={ts.priority:.2f})")
|
|||
|
|
|
|||
|
|
return "\n".join(context)
|
|||
|
|
|
|||
|
|
# ============================================================================
|
|||
|
|
# 便捷函数
|
|||
|
|
# ============================================================================
|
|||
|
|
|
|||
|
|
def analyze_rtl(rtl_code: str) -> Dict[str, Any]:
|
|||
|
|
"""
|
|||
|
|
便捷函数：分析 RTL 代码
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
rtl_code: RTL源代码字符串
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
分析结果字典
|
|||
|
|
"""
|
|||
|
|
analyzer = SemanticAnalyzer(rtl_code)
|
|||
|
|
return analyzer.analyze()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_fsm_states(rtl_code: str) -> List[str]:
|
|||
|
|
"""
|
|||
|
|
便捷函数：获取 FSM 状态列表
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
rtl_code: RTL源代码字符串
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
状态名列表
|
|||
|
|
"""
|
|||
|
|
analyzer = SemanticAnalyzer(rtl_code)
|
|||
|
|
analyzer.analyze()
|
|||
|
|
fsm_info = analyzer.get_fsm_info()
|
|||
|
|
return fsm_info['states'] if fsm_info else []
|