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rq1_coverage_improvement.py
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import pandas as pd
from common import shortNames
# IMPROVEMENT IN COVERAGE
FIELD_N = 'N'
FIELD_PROPERTY = 'Property'
FIELD_IMPROVED_LOC_COVERAGE = 'Fixed' # 'Improved LOC Coverage'
FIELD_ORIGINAL_LOC_COVERAGE = 'Vanilla' # 'Original LOC Coverage'
FIELD_LOC_COUNT_IMPROVEMENT = 'Improved' # 'Improvement'
PROP_NAMES = [FIELD_N, FIELD_PROPERTY]
CALC_NAMES = [FIELD_IMPROVED_LOC_COVERAGE, FIELD_ORIGINAL_LOC_COVERAGE, FIELD_LOC_COUNT_IMPROVEMENT]
TABLE_HEADER = PROP_NAMES + CALC_NAMES
projects = [
('convex', 'artifacts/experiment/rq1_convex.csv', 'artifacts/experiment/rq1_convex-fixed.csv'),
('jflex', 'artifacts/experiment/rq1_jflex.csv', 'artifacts/experiment/rq1_jflex-fixed.csv'),
('mphtable', 'artifacts/experiment/rq1_mph-table.csv', 'artifacts/experiment/rq1_mph-table-fixed.csv'),
('rpkicommons', 'artifacts/experiment/rq1_rpki-commons.csv', 'artifacts/experiment/rq1_rpki-commons-fixed.csv'),
]
allCoverageFile = 'artifacts/experiment/rq1_table_coverage.tex'
dataSet = pd.DataFrame()
dataSetSum = {}
rowCount = 1
for project in projects:
projName = project[0]
csvFile = project[1]
fixedCsvFile = project[2]
original = pd.read_csv(csvFile, sep=',', header=0)
original = original[ original['inPrunedGraph'] == "Y"] # only include actual reachable methods
original['entryPointKey'] = original['entryPoint'].apply(lambda v: v.split("(", 1)[0])
fixed = pd.read_csv(fixedCsvFile, sep=',', header=0)
fixed = fixed[ fixed['inPrunedGraph'] == "Y"] # only include actual reachable methods
fixed['entryPointKey'] = fixed['entryPoint'].apply(lambda v: v.split("(", 1)[0])
fixed.rename(columns=lambda x: x if x == 'entryPointKey' or x == 'method' else 'FIXED_'+x, inplace=True)
data = pd.merge(fixed, original, on=['entryPointKey', 'method'], how='left')
data['entryPoint'].fillna(data['FIXED_entryPoint'], inplace=True)
# drop rows where we don't have "FIXED"
#data = data[ data['FIXED_linesCovered'] != "UNK" ]
data['Project'] = projName
data[FIELD_ORIGINAL_LOC_COVERAGE] = data['linesCovered'].apply(lambda v: 0 if v == "UNK" else v).astype(float)
data[FIELD_IMPROVED_LOC_COVERAGE] = data['FIXED_linesCovered'].apply(lambda v: 0 if v == "UNK" else v).astype(float)
data[FIELD_LOC_COUNT_IMPROVEMENT] = 0
# add Name as a friendly name for each entrypoint
data[FIELD_PROPERTY] = data['entryPoint'].apply(lambda v: shortNames[v])
df = data[[FIELD_PROPERTY]+CALC_NAMES].groupby(by=FIELD_PROPERTY).sum().round(2)
df[FIELD_N] = pd.RangeIndex(start=rowCount, stop=len(df.index) + rowCount)
df.reset_index(inplace=True)
dfSubset = df[PROP_NAMES + CALC_NAMES]
rowCount = len(df.index) + rowCount
dataSetSum[projName] = dfSubset.copy()
dataSetSum[projName][FIELD_LOC_COUNT_IMPROVEMENT] = dataSetSum[projName][FIELD_IMPROVED_LOC_COVERAGE] - \
dataSetSum[projName][FIELD_ORIGINAL_LOC_COVERAGE]
# show only records that have improvement
dataSetSum[projName] = dataSetSum[projName][ dataSetSum[projName][FIELD_LOC_COUNT_IMPROVEMENT] > 0 ]
#dataSetSum[projName][FIELD_LOC_PERCENT_IMPROVEMENT] = \
# dataSetSum[projName][FIELD_IMPROVED_LOC_COVERAGE] / dataSetSum[projName][FIELD_ORIGINAL_LOC_COVERAGE]
#dataSetSum[projName][FIELD_METHOD_PERCENT_IMPROVEMENT] = \
# dataSetSum[projName][FIELD_IMPROVED_METHOD_COVERAGE] / dataSetSum[projName][FIELD_ORIGINAL_METHOD_COVERAGE]
dataSet = pd.concat([dataSet, data.copy()])
# output all projects with project headings
with open(allCoverageFile, 'w') as tf:
newDF = pd.DataFrame()
for project in projects:
projName = project[0]
dataSetSum[projName]['_style'] = ''
projMean = dataSetSum[projName][CALC_NAMES].mean()
projMean['_style'] = 'BOLD'
projMean[FIELD_N] = ''
projMean[FIELD_PROPERTY] = 'Average'
dataSetSum[projName].loc['mean'] = projMean
header = dict(zip(TABLE_HEADER, map(lambda v: '', TABLE_HEADER)))
newDF = pd.concat([
newDF,
pd.DataFrame(header | {'_style': 'HEADER', FIELD_PROPERTY: projName}, index=[0]), # project header
dataSetSum[projName] # project data / avg
], ignore_index=True)
bold_rows = newDF[ newDF['_style'] == 'BOLD' ].index
header_rows = newDF[ newDF['_style'] == 'HEADER' ].index
data_rows = newDF[ newDF['_style'] != 'HEADER' ].index
latexTable = newDF \
.drop(columns=['_style']) \
.style \
.hide(axis=0) \
.format({
FIELD_IMPROVED_LOC_COVERAGE: "{:.0f}",
FIELD_ORIGINAL_LOC_COVERAGE: "{:.0f}",
FIELD_LOC_COUNT_IMPROVEMENT: "+{:.0f}",
}, subset=pd.IndexSlice[data_rows, :]) \
.set_properties(subset=pd.IndexSlice[header_rows, :], **{'HEADER': ''}) \
.set_properties(subset=pd.IndexSlice[bold_rows, :], **{'textbf': '--rwrap'}) \
.to_latex(hrules=False, column_format="llrrrrrr")
outTable = ''
# transform to sub headers
for line in latexTable.splitlines(keepends=True):
s = line.split('&')
c = str(len(s))
possibleCommand = s[0].strip()
if possibleCommand == '\HEADER':
outTable += '\\hline' + "\n" + '\multicolumn{' + c + '}{c}{\\' + s[1].strip()[7:].strip() + '}' + " \\\\\n" + '\\hline' + "\n"
else:
outTable += line
tf.write(outTable)