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import functools |
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from itertools import combinations |
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from bears.c_languages.ClangBear import clang_available |
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from bears.c_languages.codeclone_detection.ClangCountVectorCreator import ( |
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ClangCountVectorCreator) |
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from bears.c_languages.codeclone_detection.ClangCountingConditions import ( |
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condition_dict) |
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from bears.c_languages.codeclone_detection.CloneDetectionRoutines import ( |
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compare_functions, |
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get_count_matrices) |
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from coalib.bears.GlobalBear import GlobalBear |
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from coalib.collecting.Collectors import collect_dirs |
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from coalib.misc.StringConverter import StringConverter |
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from coalib.results.HiddenResult import HiddenResult |
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from coalib.settings.Setting import typed_ordered_dict, path_list |
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# counting_condition_dict is a function object generated by typed_dict. This |
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# function takes a setting and creates a dictionary out of it while it |
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# converts all keys to counting condition function objects (via the |
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# condition_dict) and all values to floats while unset values default to 1. |
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counting_condition_dict = typed_ordered_dict( |
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lambda setting: condition_dict[str(setting).lower()], |
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float, |
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1) |
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default_cc_dict = counting_condition_dict(StringConverter( |
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""" |
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used: 0, |
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returned: 1.4, |
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is_condition: 0, |
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in_condition: 1.4, |
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in_second_level_condition: 1.4, |
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in_third_level_condition: 1.0, |
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is_assignee: 0, |
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is_assigner: 0.6, |
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loop_content: 0, |
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second_level_loop_content, |
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third_level_loop_content, |
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is_param: 2, |
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is_called: 1.4, |
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is_call_param: 0.0, |
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in_sum: 2.0, |
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in_product: 0, |
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in_binary_operation, |
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member_accessed""")) |
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def get_difference(function_pair, |
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count_matrices, |
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average_calculation, |
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poly_postprocessing, |
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exp_postprocessing): |
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""" |
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Retrieves the difference between two functions using the munkres algorithm. |
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:param function_pair: A tuple containing both indices for the |
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count_matrices dictionary. |
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:param count_matrices: A dictionary holding CMs. |
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:param average_calculation: If set to true the difference calculation |
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function will take the average of all variable |
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differences as the difference, else it will |
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normalize the function as a whole and thus |
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weighting in variables dependent on their size. |
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:param poly_postprocessing: If set to true, the difference value of big |
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function pairs will be reduced using a |
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polynomial approach. |
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:param exp_postprocessing: If set to true, the difference value of big |
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function pairs will be reduced using an |
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exponential approach. |
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:return: A tuple containing both function ids and their |
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difference. |
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""" |
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function_1, function_2 = function_pair |
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return (function_1, |
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function_2, |
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compare_functions(count_matrices[function_1], |
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count_matrices[function_2], |
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average_calculation, |
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poly_postprocessing, |
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exp_postprocessing)) |
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class ClangFunctionDifferenceBear(GlobalBear): |
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check_prerequisites = classmethod(clang_available) |
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def run(self, |
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counting_conditions: counting_condition_dict=default_cc_dict, |
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average_calculation: bool=False, |
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poly_postprocessing: bool=True, |
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exp_postprocessing: bool=False, |
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extra_include_paths: path_list=()): |
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''' |
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Retrieves similarities for code clone detection. Those can be reused in |
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another bear to produce results. |
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Postprocessing may be done because small functions are less likely to |
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be clones at the same difference value than big functions which may |
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provide a better refactoring opportunity for the user. |
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:param counting_conditions: A comma seperated list of counting |
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conditions. Possible values are: used, |
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returned, is_condition, in_condition, |
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in_second_level_condition, |
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in_third_level_condition, is_assignee, |
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is_assigner, loop_content, |
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second_level_loop_content, |
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third_level_loop_content, is_param, |
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in_sum, in_product, in_binary_operation, |
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member_accessed. |
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Weightings can be assigned to each |
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condition due to providing a dict |
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value, i.e. having used weighted in |
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half as much as other conditions would |
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simply be: "used: 0.5, is_assignee". |
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Weightings default to 1 if unset. |
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:param average_calculation: If set to true the difference calculation |
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function will take the average of all |
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variable differences as the difference, |
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else it will normalize the function as a |
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whole and thus weighting in variables |
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dependent on their size. |
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:param poly_postprocessing: If set to true, the difference value of big |
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function pairs will be reduced using a |
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polynomial approach. |
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:param extra_include_paths: A list containing additional include paths. |
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:param exp_postprocessing: If set to true, the difference value of big |
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function pairs will be reduced using an |
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exponential approach. |
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''' |
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self.debug("Using the following counting conditions:") |
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for key, val in counting_conditions.items(): |
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self.debug(" *", key.__name__, "(weighting: {})".format(val)) |
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self.debug("Creating count matrices...") |
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count_matrices = get_count_matrices( |
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ClangCountVectorCreator(list(counting_conditions.keys()), |
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list(counting_conditions.values())), |
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list(self.file_dict.keys()), |
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lambda prog: self.debug("{:2.4f}%...".format(prog)), |
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self.section["files"].origin, |
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collect_dirs(extra_include_paths)) |
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self.debug("Calculating differences...") |
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differences = [] |
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function_count = len(count_matrices) |
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# Thats n over 2, hardcoded to simplify calculation |
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combination_length = function_count * (function_count-1) / 2 |
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partial_get_difference = functools.partial( |
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get_difference, |
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count_matrices=count_matrices, |
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average_calculation=average_calculation, |
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poly_postprocessing=poly_postprocessing, |
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exp_postprocessing=exp_postprocessing) |
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for i, elem in enumerate( |
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map(partial_get_difference, |
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[(f1, f2) for f1, f2 in combinations(count_matrices, 2)])): |
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if i % 50 == 0: |
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self.debug("{:2.4f}%...".format(100*i/combination_length)) |
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differences.append(elem) |
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yield HiddenResult(self, differences) |
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yield HiddenResult(self, count_matrices) |
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coala-analyzer /
coala
