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| Original file line number | Diff line number | Diff line change |
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| @@ -1,3 +1,5 @@ | ||
| #!/bin/bash | ||
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| IN=inputs | ||
| IN_NAME=input.txt | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,198 @@ | ||
| #!/usr/bin/env python3 | ||
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| import pandas as pd | ||
| import fnmatch | ||
| import viz_syntax as stx | ||
| import viz_dynamic as dyn | ||
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| from all_scripts import get_all_scripts | ||
| from project_root import get_project_root | ||
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| root = get_project_root() | ||
| data_path = root / 'infrastructure/target/dynamic_analysis.jsonl' | ||
| input_size_path = root / 'infrastructure/data/size_inputs.jsonl' | ||
| loc_data_path = root / 'infrastructure/target/lines_of_code.csv' | ||
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| benchmark_category_style = { | ||
| 'bio': ('XXX', 'XXX', 'XXX'), | ||
| 'vps-audit': ('XXX', 'XXX', 'XXX'), | ||
| 'vps-audit-negate': ('XXX', 'XXX', 'XXX'), | ||
| 'aurpkg': ('XXX', 'XXX', 'XXX'), | ||
| 'makeself': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/100-files': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/read-write': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/shell-memory': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/sleep': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/time-in-shell-subprocess': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/user-time': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/user-time-in-shell': ('XXX', 'XXX', 'XXX'), | ||
| 'infrastructure/standards/write-only': ('XXX', 'XXX', 'XXX'), | ||
| 'covid-mts': ('Data analysis', 'Data extraction', '\\cite{covid-mts-source}'), | ||
| 'file-enc': ('Cryptography', 'Automation', '\\cite{cito2020empirical}'), | ||
| 'log-analysis': ('System admin.', 'Data extraction', '\\cite{spinellis2017extending, raghavan2020posh}'), | ||
| 'max-temp': ('Data analysis', 'Data extraction', '\\cite{hadoop-guide-2009}'), | ||
| 'media-conv': ('Misc.', 'Automation', '\\cite{spinellis2017extending, raghavan2020posh}'), | ||
| 'nlp': ('Machine learning', 'Text processing', '\\cite{unix-for-poets-church}'), | ||
| 'oneliners': ('Misc.', 'Text processing', '\\cite{bentley-pearl-cacm-1985, bentley-pearl-cacm-1986, unix-cacm-1974, wicked-cool-shell-scripts}'), | ||
| 'riker': ('Development', 'Build scripts', ''), | ||
| 'sklearn': ('Machine learning', 'Automation', ''), | ||
| 'uniq-ips': ('System admin.', 'Automation', ''), | ||
| 'unix50': ('Misc.', 'Text processing', '\\cite{bhandari2020solutions}'), | ||
| 'web-index': ('Development', 'Text processing', '\\cite{pash2021}') | ||
| } | ||
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| def short_category(benchmark): | ||
| dom, style, _ = benchmark_category_style[benchmark] | ||
| def shorten(str): | ||
| return ''.join([x[0].upper() for x in str.split(' ')]) | ||
| return shorten(dom) + '/' + shorten(style) | ||
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| benchmark_input_description = { | ||
| 'aurpkg': 'package files', | ||
| 'bio': 'biological data files', | ||
| 'covid-mts': 'transit data', | ||
| 'file-enc': 'pcap files', | ||
| 'log-analysis': 'log files', | ||
| 'max-temp': 'temperature data', | ||
| 'media-conv': 'media files', | ||
| 'nlp': 'text files', | ||
| 'oneliners': 'text files', | ||
| 'riker': 'source code files', | ||
| 'sklearn': 'CSV files', | ||
| 'uniq-ips': 'text files', | ||
| 'unix50': 'text files', | ||
| 'web-index': 'HTML files', | ||
| 'bio': 'XXX', | ||
| 'vps-audit': 'system status', | ||
| 'vps-audit-negate': 'system status', | ||
| 'makeself': 'XXX', | ||
| 'aurpkg': 'XXX', | ||
| 'infrastructure/standards/100-files': 'XXX', | ||
| 'infrastructure/standards/read-write': 'XXX', | ||
| 'infrastructure/standards/shell-memory': 'XXX', | ||
| 'infrastructure/standards/sleep': 'XXX', | ||
| 'infrastructure/standards/time-in-shell-subprocess': 'XXX', | ||
| 'infrastructure/standards/user-time': 'XXX', | ||
| 'infrastructure/standards/user-time-in-shell': 'XXX', | ||
| 'infrastructure/standards/write-only': 'XXX', | ||
| } | ||
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| scripts_to_include = [ | ||
| 'covid-mts/scripts/1.sh', | ||
| 'file-enc/scripts/encrypt_files.sh', | ||
| 'log-analysis/scripts/nginx.sh', | ||
| 'media-conv/scripts/img_convert.sh', | ||
| 'nlp/scripts/bigrams.sh', | ||
| 'oneliners/*', | ||
| 'unix50/scripts/1.sh', | ||
| 'riker/scripts/redis/build.sh', | ||
| # max-temp is just 1 | ||
| # sklearn is just 1 | ||
| # aurpkg is just 1 | ||
| # bio is just 1 | ||
| # makeself?? | ||
| 'web-index/scripts/ngrams.sh', | ||
| # vps-audit is just 1 | ||
| ] | ||
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| def count_unique_cmds(series): | ||
| return len({node for node in series if 'command(' in node}) | ||
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| def count_constructs(series): | ||
| return len(set(series)) | ||
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| def read_loc_data(): | ||
| loc_data = pd.read_csv(loc_data_path, header=None) | ||
| loc_data.columns = ['script', 'loc'] | ||
| loc_data['benchmark'] = loc_data['script'].apply(lambda x: x.split('/')[0]) | ||
| loc_data_bench = loc_data.groupby('benchmark').agg({ | ||
| 'loc': 'sum', | ||
| 'script': 'count' | ||
| }).reset_index() | ||
| loc_data_bench.rename(columns={'script': 'number_of_scripts'}, inplace=True) | ||
| return loc_data, loc_data_bench | ||
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| def prettify_bytes_number(n): | ||
| if n < 1024: | ||
| value, unit = n, "B" | ||
| elif n < 1024 * 1024: | ||
| value, unit = n / 1024, "KB" | ||
| elif n < 1024 * 1024 * 1024: | ||
| value, unit = n / (1024 * 1024), "MB" | ||
| else: | ||
| value, unit = n / (1024 * 1024 * 1024), "GB" | ||
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| if value < 10: | ||
| decimals = 2 | ||
| elif value < 100: | ||
| decimals = 1 | ||
| else: | ||
| decimals = 0 | ||
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| color = 'black' if unit == 'GB' else 'gray' | ||
| return f"{value:.{decimals}f} \\textcolor{{{color}}}{{{unit}}}" | ||
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| def main(): | ||
| syntax_script, syntax_bench = stx.read_data(True) | ||
| syntax_script_all_cmds, syntax_bench_all_cmds = stx.read_data(False) | ||
| dyn_script, dyn_bench = dyn.read_data() | ||
| loc_data_script, loc_data_bench = read_loc_data() | ||
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| syntax_script_all_cmds['unique_cmds'] = syntax_script_all_cmds['nodes'].apply(count_unique_cmds) | ||
| syntax_bench_all_cmds['unique_cmds'] = syntax_bench_all_cmds['nodes'].apply(count_unique_cmds) | ||
| syntax_script['constructs'] = syntax_script['nodes'].apply(count_constructs) | ||
| syntax_bench['constructs'] = syntax_bench['nodes'].apply(count_constructs) | ||
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| # all_scripts = set(syntax_script['script'].unique()) | ||
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| # missing_in_dyn = all_scripts - set(dyn_script['script'].unique()) | ||
| # missing_in_loc_data = all_scripts - set(loc_data_script['script'].unique()) | ||
| # missing_in_cmds = all_scripts - set(syntax_script_all_cmds['script'].unique()) | ||
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| # print("Missing in dyn_script:", missing_in_dyn) | ||
| # print("Missing in loc_data_script:", missing_in_loc_data) | ||
| # print("Missing in syntax_script_all_cmds:", missing_in_cmds) | ||
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| dyn_bench['input_description'] = dyn_bench['benchmark'].apply(lambda x: benchmark_input_description[x]) | ||
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| big_bench = syntax_bench.merge(dyn_bench, on='benchmark')\ | ||
| .merge(loc_data_bench, on='benchmark')\ | ||
| .merge(syntax_bench_all_cmds[['benchmark', 'unique_cmds']], on='benchmark') | ||
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| big_script = syntax_script.merge(dyn_script, on='script')\ | ||
| .merge(loc_data_script, on='script')\ | ||
| .merge(syntax_script_all_cmds[['script', 'unique_cmds']], on='script') | ||
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| print(""" | ||
| \\def\\idw{5em} | ||
| \\begin{tabular}{l|lrr|rr|l|rrrr|lr} | ||
| \\toprule | ||
| \\multirow{2}{*}{Benchmark/Script} & \\multicolumn{3}{c|}{Surface} & \\multicolumn{2}{c|}{Syntax} & \\multicolumn{1}{c|}{Inputs} & \\multicolumn{4}{c|}{Dynamic} & \\multicolumn{2}{c}{System} \\\\ | ||
| & Dom & \\#.sh & LOC & \\# Cons & \\# Cmd & & T.sh & T.cmd & Mem & I/O & \\# s/c & \\# fd \\\\ | ||
| \\midrule | ||
| """) | ||
| # generate a big latex table with the following columns: | ||
| # benchmark, short_category, number of scripts, LOC, number of constructs, number of unique commands, input description, time in shell, time in commands, max memory, IO | ||
| for _, row in big_bench.iterrows(): | ||
| numscripts_shown = 0 | ||
| numscripts = row['number_of_scripts'] | ||
| print("\\rule{0pt}{5ex}") | ||
| print(f"\\textbf{{\\tt {row['benchmark']}}} & {short_category(row['benchmark'])} & {row['number_of_scripts']} & {row['loc']} & {row['constructs']} & {row['unique_cmds']} & \\multirow{{2}}{{*}}{{\\parbox{{\\idw}}{{{prettify_bytes_number(row['input_size']) + ' of ' + row['input_description']}}}}} & {row['time_in_shell']:.2f} & {row['time_in_commands']:.2f} & {prettify_bytes_number(row['max_unique_set_size'])} & {prettify_bytes_number(row['io_chars'])} \\\\") | ||
| # now print the details of all scripts in the benchmark | ||
| for _, row_script in big_script.iterrows(): | ||
| if row_script['benchmark'] == row['benchmark'] and any([fnmatch.fnmatch(row_script['script'], pattern) for pattern in scripts_to_include]): | ||
| # all columns except leave blank benchmark, category, number of scripts, input description | ||
| print(f"\\hspace{{0.5em}} {row_script['script'].split('/')[-1]} & & & {row_script['loc']} & {row_script['constructs']} & {row_script['unique_cmds']} & & {row_script['time_in_shell']:.2f} & {row_script['time_in_commands']:.2f} & {prettify_bytes_number(row_script['max_unique_set_size'])} & {prettify_bytes_number(row_script['io_chars'])} \\\\") | ||
| numscripts_shown += 1 | ||
| if numscripts_shown < numscripts and numscripts > 1: | ||
| print(f"\\hspace{{0.5em}} \\ldots & & & & & & & & & & \\\\") | ||
| print(""" | ||
| \\bottomrule | ||
| \\end{tabular} | ||
| """) | ||
|
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| if __name__ == '__main__': | ||
| main() |
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