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import csv
import sys
import pandas as pd
from numpy import nanmedian
from scipy import stats
import matplotlib.pyplot as plt
from IPython.display import display
In [2]:
metric_names = {
'changes_size': 'Code churn',
'test_changes_size': 'Test churn',
'code_churn_overall': 'Prior changes',
'LOC': 'LOC',
'avg_cyclomatic': 'Average cyclomatic',
'cnt_func': 'Number of functions',
'maxnesting': 'Maximum nesting',
'ratio_comment': 'Comment ratio',
'modules_num': 'Module number',
'page_rank': 'PageRank',
'betweenness': 'Betweenness',
'closeness': 'Closeness',
'developer_familiarity_overall': 'Developer exp.',
'reviewer_familiarity_overall': 'Reviewer exp.',
'comments': 'Number of comments',
'comment_words': 'Comment words',
'review_duration': 'Review duration',
'min_neg': 'Developer sentiment',
'owner_neg': 'Owner sentiment',
'landing_delta': 'Landing delta',
'response_delta': 'Response delta',
'release_delta': 'Release delta',
}
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def loadMetrics():
with open('independent_metrics/metric_list.txt') as f:
return [line.rstrip('\n') for line in f]
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def loadData():
df_aurora = pd.read_csv('independent_metrics/basic_aurora.csv')
df_beta = pd.read_csv('independent_metrics/basic_beta.csv')
df_release = pd.read_csv('independent_metrics/basic_release.csv')
df_review = pd.read_csv('independent_metrics/review_metrics.csv')
df_src = pd.read_csv('independent_metrics/src_code_metrics.csv')
df_senti = pd.read_csv('independent_metrics/senti_metrics.csv')
# df_basic = pd.concat([df_aurora, df_beta, df_release]).drop_duplicates()
df_basic = df_beta
df = pd.merge(df_basic, df_review, on='bug_id')
df = pd.merge(df, df_senti, on='bug_id')
df = pd.merge(df, df_src, on='bug_id')
# Convert deltas from seconds to days.
df.landing_delta = df.landing_delta / 86400
df.response_delta = df.response_delta / 86400
df.release_delta = df.release_delta / 86400
# Ignore uplifts in the 'Pocket' component.
df = df[df.component != 'Pocket']
return df
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def subCategories(df, df_input, tag, value):
df = pd.merge(df_input, df, on='bug_id')
df_sub1 = df[df[tag] == value]
df_sub2 = df[df[tag] != value]
return df_sub1, df_sub2
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def bonferroniCorrection(p_value, num_tests):
if p_value * num_tests < 1:
return p_value * num_tests
return 1
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def outputResults(df_sub1, df_sub2, cat1):
output_list = list()
for metric in metric_list:
sub1_median = nanmedian(df_sub1[metric])
sub2_median = nanmedian(df_sub2[metric])
#print([df_sub1[metric], df_sub2[metric]])
plt.boxplot([df_sub1[metric], df_sub2[metric].dropna()], showfliers=False, labels=[cat1, 'others'])
plt.title(metric_names[metric])
plt.show()
statistic,p_value = stats.mannwhitneyu(df_sub1[metric], df_sub2[metric])
corrected_p = bonferroniCorrection(p_value, 22)
output_list.append([metric_names[metric], sub1_median, sub2_median, corrected_p])
display (pd.DataFrame(output_list, columns=['metric', cat1, 'others', 'p-value']).round(2))
return
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df = loadData()
metric_list = loadMetrics()
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df_input = pd.read_csv('manual_classification/severity_beta.csv')
#df_release = pd.read_csv('severity_release.csv')
#df_input = pd.concat([df_beta, df_release]).drop_duplicates()
df_input = df_input[df_input['Severity'].notnull()]
df_input.rename(columns={'Uplift ID':'bug_id'}, inplace=True)
df_sub1, df_sub2 = subCategories(df, df_input, 'Severity', 'R')
outputResults(df_sub1, df_sub2, 'more severe')
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df_beta = pd.read_csv('manual_classification/regressions_shipped_to_users_beta_clean.csv')
easily_preventable_beta1 = df_beta[
(df_beta[u'how_found'] == 'found on a widely used feature/website/config') &
((df_beta[u'reproducible'] == 'reproducible') | (df_beta[u'reproducible'] == 'reproducible (but not by everyone)'))
]
easily_preventable_beta2 = df_beta[
(df_beta[u'how_found'] == 'found via telemetry') &
((df_beta[u'reproducible'] == 'not reproducible') | (df_beta[u'reproducible'] == 'not reproducible (except by reporter)'))
]
df_input = pd.concat([easily_preventable_beta1,easily_preventable_beta2])
df_input.rename(columns={'uplift_id':'bug_id'}, inplace=True)
df_sub1, df_sub2 = subCategories(df, df_input, 'reproducible', 'reproducible')
outputResults(df_sub1, df_sub2, 'possibly preventable')
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def partiallyFixed(filename):
partially_fixed_bugs = set()
with open('manual_classification/%s' %filename) as f:
reader = csv.reader(f)
for row in reader:
if 'partially fixed' in row[-2]:
partially_fixed_bugs.add(int(row[0]))
return partially_fixed_bugs
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partially_fixed_bugs = partiallyFixed('additionally_uplifted_with_channels.csv')
partially_fixed_bugs |= partiallyFixed('bm25_opened_after_with_channels.csv')
partially_fixed_bugs |= partiallyFixed('bm25_resolved_after_with_channels.csv')
partially_fixed_bugs |= partiallyFixed('cloned_with_channels.csv')
partially_fixed_bugs |= partiallyFixed('reopened_with_channels.csv')
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df_sub1 = df[df['bug_id'].isin(partially_fixed_bugs)]
df_sub2 = df[~df['bug_id'].isin(partially_fixed_bugs)]
outputResults(df_sub1, df_sub2, 'partially fixed')
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