| import datetime |
| import random |
| import string |
|
|
| import numpy as np |
| import rstr |
| from fuzzer_dir.schema_validator import validate |
| from fuzzer_dir.runtime_dictionary import RuntimeDictionary |
| from fuzzer_dir.constant import ValueSource |
| from model.parameter import TargetType, TargetStatus |
| from model.method import Method |
| import uuid |
|
|
|
|
| class RandomDataGenerator: |
| SKIP_OPTIONAL = "SKIP_OPTIONAL" |
|
|
| def __init__(self, parameter, runtime_dictionary: RuntimeDictionary, ref_dict: {}, method: Method): |
| self.parameter = parameter |
| self.parameter_name = parameter.name |
| self.runtime_dictionary = runtime_dictionary |
| self.ref_dict = ref_dict |
| self.ref_prefix = "%{APIGen" |
| self.ref_suffix = "}" |
| self.threshold = 0.7 |
| self.validate_value = False |
| self.refs_varabile = set() |
| self.value_source_count = {} |
| self.potential_targets = set() |
| self.value_source = {} |
| self.method = method |
|
|
| def generate(self): |
| if self.should_skip_optional_parameter(self.parameter.raw_body): |
| self.record_source(ValueSource.skip) |
| |
| return RandomDataGenerator.SKIP_OPTIONAL |
| if self.is_simple_request_parameter(self.parameter.raw_body): |
| val = self.value_factory(self.parameter_name, self.parameter.raw_body) |
| else: |
| val = self.value_factory("", self.parameter.raw_body) |
| |
| if self.validate_value: |
| validate(val, self.parameter.raw_body) |
| assert val is not None, f'{self.parameter.name}' |
| return val |
|
|
| def is_simple_request_parameter(self, parameter_body): |
| if parameter_body.__contains__("in") and parameter_body["in"] == "body": |
| return False |
| return True |
|
|
| def get_value_source_stat(self): |
| return self.value_source_count |
|
|
| def record_source(self, source_type): |
| self.value_source_count[source_type] = self.value_source_count.get(source_type, 0) + 1 |
|
|
| def _build_ref(self, property): |
| refs = '#'.join([self.ref_dict.method.method_signature + i for i in list(property)]) |
| refs = '#' + refs |
| self.refs_varabile = self.refs_varabile.union(property) |
| |
| return f'{self.ref_prefix}{refs}{self.ref_suffix}' |
|
|
| def should_use_example(self, path, parameter_body): |
| |
| if isinstance(parameter_body, dict) and (parameter_body.__contains__('example') or parameter_body.__contains__( |
| 'x-example')) and np.random.random() < 0.5: |
| return True |
| return False |
|
|
| def should_skip_optional_parameter(self, parameter_body): |
| if parameter_body.__contains__('required') and parameter_body['required'] is True: |
| return False |
| if parameter_body.__contains__('in') and parameter_body['in'] in ['path', 'body']: |
| return False |
| |
| if np.random.random() < 0.4: |
| return False |
| return True |
|
|
| def should_skip_optional_property(self, parameter_body, key): |
| if parameter_body.__contains__('required') and key in parameter_body['required']: |
| return False |
| if parameter_body.__contains__('in') and parameter_body['in'] in ['path', 'body']: |
| return False |
| if np.random.random() < 0.4: |
| return False |
| return True |
|
|
| def use_example(self, path, parameter_body): |
| potential_target = (path, TargetType.EXAMPLE, TargetStatus.EXAMPLE) |
| self.potential_targets.add(potential_target) |
| if parameter_body.__contains__('example'): |
| example = parameter_body['example'] |
| else: |
| example = parameter_body['x-example'] |
| val = example |
| return val |
|
|
| def value_factory(self, path, parameter): |
| if self.should_use_example(path, parameter): |
| val = self.use_example(path, parameter) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| assert ref is not None, 'ref is None'f'{path}' |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| param_type = self.check_parameter_type(path, parameter) |
| if param_type == "integer": |
| val = self.integer_factory(path, parameter) |
| assert val is not None, 'val(integer) is None'f'{path}' |
| return val |
| elif param_type == "string": |
| val = self.string_factory(path, parameter) |
| assert val is not None, 'val(string) is None'f'{path}' |
| return val |
| elif param_type == "file": |
| val = self.string_factory(path, parameter) |
| assert val is not None, 'val(file) is None'f'{path}' |
| return val |
| elif param_type == 'array': |
| if self.should_use_example(path, parameter): |
| val = self.use_example(path, parameter) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, parameter) |
| if isinstance(value, list): |
| try: |
| validate(value, parameter) |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| except Exception: |
| pass |
| items = parameter["items"] |
| array = [] |
| for i in range(np.random.choice(range(0, 2))): |
| item = self.value_factory(f'{path}[0]', items) |
| if self.should_skip_optional_parameter(items): |
| path_signature = f'{path}[{i}]_skip' |
| self.record_source(ValueSource.skip) |
| |
| continue |
| assert item is not None, f'val(array,item) is None'f'{path}' |
| array.append(item) |
| self.value_source[f'{path}[0]'] = (item, ValueSource.random) |
| |
| |
| |
| if len(array) == 0: |
| potential_target = (path, TargetType.ARRAY, TargetStatus.EMPTY) |
| else: |
|
|
| potential_target = (path, TargetType.ARRAY, TargetStatus.NON_EMPTY) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (array, ValueSource.random) |
| return array |
| elif param_type == 'schema': |
| val = self.build_schema(path, parameter["schema"]) |
| assert val is not None, f'val(array,item) is None'f'{path},{parameter}' |
| return val |
| elif param_type == "properties": |
| parameter['type'] = 'object' |
| val = self.build_schema(path, parameter) |
| assert val is not None, f'val(properties) is None'f'{path}' |
| return val |
| elif param_type == 'object': |
| val = self.object_factory(path, parameter) |
| assert val is not None, f'val(object) is None'f'{path}' |
| return val |
| elif param_type == 'boolean': |
| val = self.boolean_factory(path) |
| assert val is not None, f'val(boolean) is None'f'{path}' |
| return val |
| elif param_type == "allOf": |
| if self.should_use_example(path, parameter): |
| val = self.use_example(path, parameter) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, parameter) |
| if isinstance(value, dict): |
| try: |
| validate(value, parameter) |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| except: |
| pass |
| result = {} |
| for item in parameter["allOf"]: |
| elem = self.value_factory(path, item) |
| |
| |
| |
| assert elem is not None, f'val(allOf) is None'f'{path}' |
| for key in elem.keys(): |
| if self.should_skip_optional_property(item, key): |
| path_signature = f'{path}.{key}' |
| self.record_source(ValueSource.skip) |
| |
| continue |
| result[key] = elem[key] |
| self.value_source[path] = (result, ValueSource.random) |
| return result |
| elif param_type == 'number': |
| val = self.number_factory(path, parameter) |
| assert val is not None, 'val(number) is None'f'{path}' |
| return val |
| elif param_type == 'skip': |
| pass |
| else: |
| raise Exception("Unrecognized type in parameter", parameter) |
|
|
| def check_parameter_type(self, path, parameter={}): |
| param_type = None |
| if parameter.__contains__("type") and isinstance(parameter['type'], str |
| ): |
| param_type = parameter["type"] |
| elif parameter.__contains__("schema"): |
| param_type = "schema" |
| elif parameter.__contains__("properties"): |
| param_type = "properties" |
| elif parameter.__contains__("allOf"): |
| param_type = "allOf" |
| elif parameter.__contains__('number'): |
| param_type = 'number' |
| |
| |
| else: |
| raise Exception(f"unknown parameter type: {parameter}") |
| return param_type |
|
|
| def build_schema(self, path, schema={}): |
| if self.should_use_example(path, schema): |
| val = self.use_example(path, schema) |
| self.record_source(ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| return ref |
| try: |
| if isinstance(schema['type'], str): |
| schema_type = schema["type"] |
| else: |
| raise Exception('not a schema type') |
| except: |
| if schema.__contains__("properties"): |
| schema_type = "object" |
| elif schema.__contains__("items"): |
| schema_type = "array" |
| elif schema.__contains__('allOf'): |
| schema_type = 'allOf' |
| else: |
| schema_type = "object" |
| if schema_type == "object": |
| return self.object_factory(path, schema) |
| if schema_type == "array": |
| if self.should_use_example(path, schema): |
| self.record_source(ValueSource.example) |
| val = self.use_example(path, schema) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| self.record_source(ValueSource.reference) |
| ref = self._build_ref(self.ref_dict[path]) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
|
|
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, schema) |
| if isinstance(value, list): |
| try: |
| validate(value, schema) |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| except: |
| pass |
| items = schema["items"] |
| array = [] |
| for i in range(np.random.choice(range(0, 2))): |
| res = self.value_factory(f'{path}[0]', items) |
| if self.should_skip_optional_parameter(items): |
| path_signature = f'{path}[0]' |
| self.record_source(ValueSource.skip) |
| |
| continue |
| array.append(res) |
| self.value_source[f'{path}[0]'] = (res, ValueSource.random) |
|
|
| if len(array) == 0: |
| potential_target = (path, TargetType.ARRAY, TargetStatus.EMPTY) |
| else: |
| potential_target = (path, TargetType.ARRAY, TargetStatus.NON_EMPTY) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (array, ValueSource.random) |
| return array |
| if schema_type == 'allOf': |
| if self.should_use_example(path, schema): |
| val = self.use_example(path, schema) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, schema) |
| if isinstance(value, dict): |
| try: |
| validate(value, schema) |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| except: |
| pass |
| result = {} |
| for item in schema["allOf"]: |
| elem = self.value_factory(path, item) |
| |
| |
| |
| |
| assert elem is not None, f'val(allOf) is None'f'{path}' |
| for key in elem.keys(): |
| if self.should_skip_optional_property(item, key): |
| path_signature = f'{path}.{key}' |
| self.record_source(ValueSource.skip) |
| |
| continue |
| result[key] = elem[key] |
| self.value_source[path] = (result, ValueSource.random) |
| return result |
| val = self.value_factory(path, schema) |
| self.value_source[path] = (val, ValueSource.random) |
| return val |
|
|
| def should_use_dictionary_value(self, path: str): |
| if not self.runtime_dictionary.has_candidate_in_dictionary(path): |
| self.runtime_dictionary.calculate_path_threshold(path) |
| |
| |
| return self.runtime_dictionary.should_use_dictionary(path) |
|
|
| def string_factory(self, path, string_body={}): |
| if self.should_use_example(path, string_body): |
| val = self.use_example(path, string_body) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| min_len = 0 |
| max_len = 100 |
| self.record_source(ValueSource.random) |
| if string_body.__contains__("enum"): |
| self.record_source(ValueSource.enum) |
| enum = np.random.choice(string_body["enum"]) |
| potential_target = (path, TargetType.ENUM, enum) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (enum, ValueSource.enum) |
| return enum |
|
|
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, string_body) |
| if isinstance(value, str): |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| if string_body.__contains__("minLength") and string_body.__contains__("maxLength"): |
| min_len = string_body["minLength"] |
| max_len = string_body["maxLength"] |
|
|
| if string_body.__contains__("minLength") and np.random.random() < 0.2: |
| max_len = string_body["minLength"] |
| potential_target = (path, TargetType.STRING, TargetStatus.MIN) |
| self.potential_targets.add(potential_target) |
|
|
| elif string_body.__contains__("maxLength") and np.random.random() < 0.2: |
| min_len = string_body["maxLength"] |
| potential_target = (path, TargetType.STRING, TargetStatus.MAX) |
| self.potential_targets.add(potential_target) |
|
|
| elif string_body.__contains__("minLength") and string_body.__contains__("maxLength"): |
| potential_target = (path, TargetType.STRING, TargetStatus.MIDDLE) |
| self.potential_targets.add(potential_target) |
|
|
| if string_body.__contains__('format'): |
| self.record_source(ValueSource.random) |
| format = string_body['format'] |
| if format == 'date-time': |
| res = datetime.datetime.now().isoformat('T') |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
| elif format == 'uuid': |
| res = uuid.uuid4().__str__() |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
| else: |
| raise Exception('unknown string format') |
| if string_body.__contains__('pattern'): |
| self.record_source(ValueSource.random) |
| pattern = string_body['pattern'] |
| res = rstr.xeger(pattern) |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
| |
| max_len = min(max_len, 100) |
| if max_len <= min_len: |
| str_len = max_len |
| else: |
| str_len = np.random.randint(min_len, max_len + 1) |
| res = ''.join(random.choices(string.ascii_uppercase + |
| string.digits, k=str_len)) |
| if str_len == min_len: |
| self.record_source(ValueSource.min) |
| self.value_source[path] = (res, ValueSource.min) |
| elif str_len == max_len: |
| self.record_source(ValueSource.max) |
| self.value_source[path] = (res, ValueSource.max) |
| else: |
| self.record_source(ValueSource.middle) |
| self.value_source[path] = (res, ValueSource.middle) |
| return res |
|
|
| def object_factory(self, path, object_body={}): |
| if self.should_use_example(path, object_body): |
| val = self.use_example(path, object_body) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, object_body) |
| if isinstance(value, dict): |
| try: |
| validate(value, object_body) |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| except: |
| pass |
| self.record_source(ValueSource.random) |
| data = {} |
| if object_body.__contains__("properties"): |
| for name in object_body["properties"]: |
| if len(path) > 0: |
| property_path = f'{path}.{name}' |
| else: |
| property_path = name |
| |
| |
| |
| res = self.value_factory(property_path, object_body["properties"][name]) |
| if self.should_skip_optional_property(object_body, |
| name): |
| if len(path) > 0: |
| path_signature = f'{path}.{name}' |
| else: |
| path_signature = name |
| self.record_source(ValueSource.skip) |
| |
| continue |
| data[name] = res |
| else: |
| for name in object_body.keys(): |
| if name == 'additionalProperties': |
| continue |
| if len(path) > 0: |
| property_path = f'{path}.{name}' |
| else: |
| property_path = name |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| self.value_source[path] = (data, ValueSource.random) |
| return data |
|
|
| def boolean_factory(self, path): |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
| res = np.random.choice(['true', 'false']) |
| if res == 'true': |
| potential_target = (path, TargetType.BOOL, TargetStatus.TRUE) |
| else: |
| potential_target = (path, TargetType.BOOL, TargetStatus.FALSE) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
|
|
| def number_factory(self, path, number_body={}): |
| if self.should_use_example(path, number_body): |
| val = self.use_example(path, number_body) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
|
|
| if number_body.__contains__("enum"): |
| self.record_source(ValueSource.enum) |
| enum = np.random.choice(number_body["enum"]) |
| potential_target = (path, TargetType.ENUM, enum) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (enum, ValueSource.enum) |
| return enum |
|
|
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, number_body) |
| if isinstance(value, float): |
| self.record_source(ValueSource.dictionary) |
| self.value_source[path] = (value, ValueSource.dictionary) |
| return value |
| |
| if np.random.random() < 0.5: |
| res = np.random.randint(0, 2) |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
|
|
| if number_body.__contains__("minimum") and number_body.__contains__("maximum"): |
| if np.random.random() < 0.5: |
| res = np.random.randint(number_body["minimum"], number_body["maximum"], dtype=np.int64) |
| potential_target = (path, TargetType.NUM, TargetStatus.MIDDLE) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| else: |
| res = np.random.choice([number_body["minimum"], number_body["maximum"]]) |
| if res == number_body['minimum']: |
| potential_target = (path, TargetType.NUM, TargetStatus.MIN) |
| self.record_source(ValueSource.min) |
| self.value_source[path] = (res, ValueSource.min) |
| self.value_source[path] = (res, ValueSource.min) |
| else: |
| potential_target = (path, TargetType.NUM, TargetStatus.MAX) |
| self.record_source(ValueSource.max) |
| self.value_source[path] = (res, ValueSource.max) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.max) |
| return res |
| elif number_body.__contains__("minimum"): |
| if np.random.random() < 0.2: |
| res = number_body["minimum"] |
| potential_target = (path, TargetType.NUM, TargetStatus.MIN) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.min) |
| self.record_source(ValueSource.min) |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| return res |
| elif number_body.__contains__("maximum"): |
| if np.random.random() < 0.2: |
| res = number_body["maximum"] |
| potential_target = (path, TargetType.NUM, TargetStatus.MAX) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.max) |
| self.record_source(ValueSource.max) |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| return res |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| return res |
|
|
| def integer_factory(self, path, integer_body={}): |
| if self.should_use_example(path, integer_body): |
| val = self.use_example(path, integer_body) |
| self.record_source(ValueSource.example) |
| self.value_source[path] = (val, ValueSource.example) |
| return val |
| if path in self.ref_dict: |
| ref = self._build_ref(self.ref_dict[path]) |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (ref, ValueSource.reference) |
| return ref |
|
|
| if integer_body.__contains__("enum"): |
| self.record_source(ValueSource.enum) |
| enum = np.random.choice(integer_body["enum"]) |
| potential_target = (path, TargetType.ENUM, enum) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (enum, ValueSource.enum) |
| return enum |
|
|
| if self.should_use_dictionary_value(path): |
| value = self.runtime_dictionary.generate_value_from_dictionary(path, self.method, integer_body) |
| if isinstance(value, int): |
| self.record_source(ValueSource.reference) |
| self.value_source[path] = (value, ValueSource.reference) |
| return value |
| |
| if np.random.random() < 0.5: |
| res = np.random.randint(0, 2) |
| self.value_source[path] = (res, ValueSource.random) |
| self.record_source(ValueSource.random) |
| return res |
| if integer_body.__contains__("minimum") and integer_body.__contains__("maximum"): |
| if np.random.random() < 0.5: |
| res = np.random.randint(integer_body["minimum"], integer_body["maximum"], dtype=np.int64) |
| potential_target = (path, TargetType.NUM, TargetStatus.MIDDLE) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| else: |
| res = np.random.choice([integer_body["minimum"], integer_body["maximum"]]) |
| if res == integer_body['minimum']: |
| potential_target = (path, TargetType.NUM, TargetStatus.MIN) |
| self.value_source[path] = (res, ValueSource.min) |
| self.record_source(ValueSource.min) |
| else: |
| potential_target = (path, TargetType.NUM, TargetStatus.MAX) |
| self.value_source[path] = (res, ValueSource.max) |
| self.record_source(ValueSource.max) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
| elif integer_body.__contains__("minimum"): |
| if np.random.random() < 0.2: |
| res = integer_body["minimum"] |
| potential_target = (path, TargetType.NUM, TargetStatus.MIN) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.min) |
| self.record_source(ValueSource.min) |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| self.value_source[path] = (res, ValueSource.random) |
| return res |
| elif integer_body.__contains__("maximum"): |
| if np.random.random() < 0.2: |
| res = integer_body["maximum"] |
| potential_target = (path, TargetType.NUM, TargetStatus.MAX) |
| self.potential_targets.add(potential_target) |
| self.value_source[path] = (res, ValueSource.max) |
| self.record_source(ValueSource.max) |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.middle) |
| self.record_source(ValueSource.middle) |
| return res |
| else: |
| res = np.random.randint(0, 999999) |
| self.value_source[path] = (res, ValueSource.random) |
| self.record_source(ValueSource.random) |
| return res |
|
|