135 lines
3.8 KiB
Python
Executable file
135 lines
3.8 KiB
Python
Executable file
#!/usr/bin/env python3
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import javalang
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import os
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import pandas as pd
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import glob
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import re
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DIR: str = os.path.dirname(os.path.realpath(__file__))
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SOURCES: str = DIR + '/sources/src/com/google/javascript/jscomp/'
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OUT_DIR: str = DIR + '/metrics'
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def clean_output():
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filelist = glob.glob(OUT_DIR + '/*.csv')
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for f in filelist:
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os.remove(f)
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def count(node: javalang.tree.Node, the_filter) -> int:
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i = 0
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for _, _ in node.filter(the_filter):
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i += 1
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return i
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def metrics(clazz: javalang.tree.ClassDeclaration) -> dict[str, str]:
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# Method metrics
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m = {
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'MTH': len(clazz.methods),
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'FLD': len(clazz.fields),
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'RFC': 0
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}
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# RFC: #Public methods + #Method invocations
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for method in clazz.methods:
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if 'public' in method.modifiers:
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m['RFC'] += 1
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m['RFC'] += count(clazz, javalang.tree.MethodInvocation)
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# INT: #Implemented interfaces
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m['INT'] = 0 if clazz.implements is None else len(clazz.implements)
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# Method metrics (max across methods)
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sz = 0
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cpx = 0
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ex = 0
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ret = 0
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sum_m_name_len = 0
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for method in clazz.methods:
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sum_m_name_len += len(method.name)
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# SZ: #Statements
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sz = max(sz, count(method, javalang.tree.Statement))
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# CPX: #CONDITIONAL + #LOOP statements
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cpx = max(cpx,
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count(method, javalang.tree.IfStatement) +
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count(method, javalang.tree.TernaryExpression) +
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count(method, javalang.tree.WhileStatement) +
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count(method, javalang.tree.ForStatement))
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# EX: #Exceptions in throws clause
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ex = max(ex, 0 if method.throws is None else len(method.throws))
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# RET: #Return points
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ret = max(ret, count(method, javalang.tree.ReturnStatement))
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m['SZ'] = sz
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m['CPX'] = cpx
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m['EX'] = ex
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m['RET'] = ret
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# NLP metrics
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# Average length of method names
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l = len(clazz.methods)
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m['NML'] = 0 if l == 0 else sum_m_name_len / l
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m['BCM'] = 0
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m['WRD'] = 0
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for _, node in clazz.filter(javalang.tree.Documented):
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if node.documentation is not None:
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# BCM: #block comments
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m['BCM'] += 1
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# #Words (the longest alphanumeric substrings) in block comments
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m['WRD'] += len(re.findall('\\w+', node.documentation))
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# #Words in comments / #Statements
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s = count(clazz, javalang.tree.Statement)
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m['DCM'] = 0 if s == 0 else m['WRD'] / s
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return m
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def create_df(root) -> pd.DataFrame:
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df = pd.DataFrame(columns=['class_name', 'MTH', 'FLD', 'RFC',
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'INT', 'SZ', 'CPX', 'EX', 'RET', 'BCM', 'NML', 'WRD', 'DCM'])
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i: int = 0
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for path, dirs, files in os.walk(root):
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for f in files:
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if f.endswith('.java'):
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# Get the AST of the file
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with open(path + '/' + f) as file:
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data = file.read()
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tree = javalang.parse.parse(data)
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# Fetch package name from package declaration
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# if node is missing, assuming default package ('')
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package_name = ''
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for _, node in tree.filter(javalang.tree.PackageDeclaration):
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package_name = node.name
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break
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# Get all classes and number of methods for each one
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for _, node in tree.filter(javalang.tree.ClassDeclaration):
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fqdn = package_name + '.' + node.name
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df.loc[i, 'class_name'] = fqdn
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m = metrics(node)
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for metric in m:
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df.loc[i, metric] = m[metric]
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i += 1
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return df
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def main():
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clean_output()
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df = create_df(SOURCES)
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df.to_csv(OUT_DIR + '/feature_vectors.csv', index=False)
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if __name__ == '__main__':
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main()
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