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ima01/find_god_classes.py

79 lines
2.1 KiB
Python
Executable file

#!/usr/bin/env python3
import javalang
import os
import pandas as pd
import glob
import tabulate
# God class if:
# |M(C)| > E(M) + 6*V(M)
# (number of methods greater than average across all classes plus 6 times the
# standard deviation)
DIR: str = os.path.dirname(os.path.realpath(__file__))
SOURCES: str = DIR + '/resources/xerces2-j-src'
OUT_DIR: str = DIR + '/god_classes'
def clean_output():
filelist = glob.glob(OUT_DIR + '/*.csv')
for f in filelist:
os.remove(f)
def parse(path: str) -> list[tuple[str, str]]:
# Get the AST of the file
with open(path) as file:
data = file.read()
tree = javalang.parse.parse(data)
# Fetch package name from package declaration
# if node is missing, assuming default package ('')
package_name = ''
for _, node in tree.filter(javalang.tree.PackageDeclaration):
package_name = node.name
break
# Get all classes and number of methods for each one
rows: list[tuple[str, str]] = []
for _, node in tree.filter(javalang.tree.ClassDeclaration):
fqdn = package_name + '.' + node.name
rows.append((fqdn, len(node.methods),))
return rows
def create_df(root) -> pd.DataFrame:
frame = pd.DataFrame(columns=['class_name', 'method_num'])
i: int = 0
for path, dirs, files in os.walk(root):
for f in files:
if f.endswith('.java'):
# for each java file, add all entries found to dataframe
for row in parse(path + '/' + f):
frame.loc[i] = row
i += 1
return frame
def main():
clean_output()
df = create_df(SOURCES)
mean = df.loc[:, 'method_num'].mean()
std = df.loc[:, 'method_num'].std()
threshold = mean + 6 * std
god_classes_df = df[df['method_num'] > threshold]
god_classes_df.to_csv(OUT_DIR + '/god_classes.csv')
god_classes_df.columns = ['Class Name', '# Methods']
god_classes_df = god_classes_df.set_index('Class Name', drop=True)
print(god_classes_df.to_markdown())
if __name__ == '__main__':
main()