How to use exceptions_csv method in locust

Best Python code snippet using locust

tkl_coverage_analysis.py

Source:tkl_coverage_analysis.py

1import errno2import json3import numpy as np4import os5import pandas as pd6import plotly.express as px7import re8import sys9import toml10from pandas.core.dtypes.common import is_numeric_dtype11from pathlib import Path12import apps_list13pd.options.mode.chained_assignment = None  # default='warn'14COLUMNS = ['App', 'Time limit', 'Interaction Level', 'Combination', 'Trial', 'Averaging criterion',15           'Instruction coverage', 'Branch coverage', 'Method coverage', 'Test methods', 'CTD coverage']16def calc_coverage(coverage_file, precision):17    all_data = pd.read_csv(coverage_file)18    cov_sums = all_data.sum(axis=0)19    inst_cov = round(cov_sums['INSTRUCTION_COVERED'] / (cov_sums['INSTRUCTION_COVERED'] + cov_sums['INSTRUCTION_MISSED']), precision)20    branch_cov = round(cov_sums['BRANCH_COVERED'] / (cov_sums['BRANCH_COVERED'] + cov_sums['BRANCH_MISSED']), precision)21    method_cov = round(cov_sums['METHOD_COVERED'] / (cov_sums['METHOD_COVERED'] + cov_sums['METHOD_MISSED']), precision)22    return inst_cov, branch_cov, method_cov23def count_test_methods(test_file_list):24    num_methods = 025    for test_file in test_file_list:26        with open(test_file, 'r') as f:27            lines = f.readlines()28            r = re.compile(r'[\t ]*@Test(?:\(time_limit ?= ?[0-9]+\))?[\t ]*')29            evo_none = re.compile(r'EvoSuite did not generate any tests')30            for i, line in enumerate(lines):31                if r.match(line):32                    num_methods += 133                if evo_none.search(line):34                    num_methods -= 135    return num_methods36def snake_case(s, pre='', post=''):37    if pre:38        pre += ' '39    if post:40        post = ' ' + post41    return re.sub(r'[()]', '', re.sub(r'[ ,.-]', '_', pre + s.lower() + post))42def open_csv(csv_file, columns, categorized=True):43    with open(csv_file) as f:44        df = pd.read_csv(f)45        if categorized:46            categories = list(df.columns[1 + len(columns):])47            df.columns = ['0'] + columns + categories48            return df.drop('0', axis=1), categories49        else:50            df.columns = ['0'] + columns51            return df.drop('0', axis=1)52def print_app_df(apps, avg_df, name_df, images_folder, categories, rng_y):53    current_images_folder_base = os.path.join(images_folder, name_df)54    desired_columns = ['Instruction coverage', 'Branch coverage', 'Method coverage'] + categories55    # create scattered plot per app, averaging trials and sampling trials alike56    current_images_folder = os.path.join(current_images_folder_base, 'per-app')57    Path(current_images_folder).mkdir(parents=True, exist_ok=True)58    for app in apps:59        print('Plotting coverage graphs for app ' + app)60        app_df = avg_df[(avg_df['App'] == app)]61        if app_df.empty:62            continue63        for column in desired_columns:64            app_df_filtered = app_df[(app_df[column].astype(str) != 'nan')].astype({column: float})65            if not app_df_filtered.empty:66                app_df_filtered.sort_values(by=['Interaction Level', 'Combination'], axis=0, inplace=True)67                fig = px.line(app_df_filtered, x='Interaction Level', y=column, color='Configuration',68                              range_y=rng_y)69                fig.write_image(os.path.join(current_images_folder, f'{app}_{snake_case(column)}.png'), format='png')70                print('Written image ' + os.path.join(current_images_folder, f'{app}_{snake_case(column)}.png'))71def print_all_df(all_df, name_df, images_folder, categories, config):72    width, rng_y, pts = config['analyze']['width'], config['analyze']['rng_y'], config['analyze']['pts']73    all_df.replace('ctdamplified', 'ctdguided', regex=True, inplace=True)74    current_images_folder_base = os.path.join(images_folder, name_df)75    desired_columns = ['Instruction coverage', 'Branch coverage', 'Method coverage'] + categories76    # create box plot for all data, no averaging77    current_images_folder = os.path.join(current_images_folder_base, 'all-apps')78    Path(current_images_folder).mkdir(parents=True, exist_ok=True)79    print('Plotting coverage graphs for all data')80    for column in desired_columns:81        all_df_filtered = all_df[(all_df[column].astype(str) != 'nan')].astype({column: float})82        if not all_df_filtered.empty:83            all_df_filtered.sort_values(by=['Interaction Level', 'Combination'], axis=0, inplace=True)84            fig = px.box(all_df_filtered, x='Interaction Level', y=column, color='Configuration', points=pts,85                         width=width, range_y=rng_y).update_traces(boxmean=True).update_layout(86                font=dict(87                    size=18,88                ),89                legend=dict(90                    orientation="h",91                    yanchor="bottom",92                    y=1.002,93                    xanchor="right",94                    x=1,95                ),96                legend_title_text='',97            )98            fig.write_image(os.path.join(current_images_folder, f'{snake_case(column)}.png'), format='png')99            print('Written image ' + os.path.join(current_images_folder, f'{snake_case(column)}.png'))100            all_df_filtered.to_csv(os.path.join(images_folder, '..', f'{name_df}_all-apps_{snake_case(column)}.csv'))101def print_all_csv_cumulate(all_csvs, name_csv, columns, images_folder, config):102    width, rng_y, pts = config['analyze']['width'], config['analyze']['rng_y'], config['analyze']['pts']103    all_dfs = []104    categories = None105    for all_csv in all_csvs:106        all_df, categories_all = open_csv(all_csv, columns, categorized=True)107        all_dfs.append(all_df)108        if not categories:109            categories = categories_all110        else:111            if categories != categories_all:112                print(f'{categories} (from file {all_csvs[0]}) is different from {categories_all} (from file {all_csv})', file=sys.stderr)113                assert False114    all_df = pd.concat(all_dfs, ignore_index=True)115    print_all_df(all_df, name_csv, images_folder, categories[:-3], config)  # -3 because we add 3 columns after categories116def run_coverage_analysis_separate(app: str, config, categorizer_output):117    columns = COLUMNS118    time_limits, levels, trials, output_dir, combs = \119        config['general']['time_limits'], config['general']['levels'], config['general']['trials'], \120        config['general']['output_dir'], config['general']['combs']121    verbose, precision = config['verbosity']['verbose'], config['verbosity']['precision']122    width, rng_y, pts, plots_per_app, delete_intermediate, analyze_categories, images_dir, force = \123        config['analyze']['width'], config['analyze']['rng_y'],  config['analyze']['pts'],\124        config['analyze']['plots_per_app'], config['analyze']['delete_intermediate'], \125        config['analyze']['analyze_categories'], config['analyze']['images_dir'], config['analyze']['force']126    categories = []  # ['Instruction coverage (0)', 'Instruction coverage (1)', 'Instruction coverage (under)',127    #                   'Instruction coverage (over)', 'Instruction coverage (col)', 'Instruction coverage (total)']128    output_dir_abs = os.path.abspath(output_dir)129    app_dir = os.path.join(output_dir_abs, app)130    if os.path.exists(os.path.join(app_dir, f'{app}_cov_analysis_results.csv')) and not force:131        print(f'Skipped app {app} (results exist)')132        return133    df_apps = []134    df_time_limits = []135    df_int_levels = []136    df_combs = []137    df_trials = []138    df_averaging_criterion = []139    df_inst_cov = []140    df_branch_cov = []141    df_method_cov = []142    df_n_tests = []143    df_ctd_cov = []144    cov_df: pd.DataFrame145    if delete_intermediate:146        def remove_noexcept(filename, verbose=False):147            try:148                os.remove(filename)149                if verbose:150                    print(f'Deleted file {os.path.basename(filename)}')151            except OSError as e:152                if e.errno != errno.ENOENT:153                    raise154        remove_noexcept(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate.csv'), True)155        remove_noexcept(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate2.csv'), True)156    if os.path.exists(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate2.csv')):157        cov_df, categories = open_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate2.csv'), columns, categorized=True)158        print('Loaded DataFrame from saved second intermediate file')159        print(f'Implied categories: {categories}')160    else:161        if os.path.exists(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate.csv')):162            cov_df = open_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate.csv'),163                              columns, categorized=False)164            print('Loaded DataFrame from saved intermediate file')165        else:166            for app_config in os.listdir(app_dir):167                if '.' in app_config or 'experiment' in app_config:168                    continue169                short_config = app_config[app_config.find(app + '_') + len(app + '_'):]170                time_limit = short_config[:short_config.find('s_')]171                if not int(time_limit) in time_limits:172                    continue173                interaction_level = short_config[short_config.find('s_') + 2:short_config.find('l_')]174                if not int(interaction_level) in levels:175                    continue176                trial = short_config[short_config.rfind('_') + 1:]177                if not int(trial) in trials:178                    continue179                print('App: {} time limit: {}, interaction_level: {}, trial: {}'.format(app, time_limit,180                                                                                        interaction_level, trial))181                reports_dir = os.path.join(app_dir, app_config, app_config + "-tkltest-reports")182                ctd_cov_file = os.path.join(app_dir, app_config, app_config + "_test_generation_summary.json")183                if not os.path.isfile(ctd_cov_file):184                    print("** ERROR: ctd coverage file {} doesn't exist".format(ctd_cov_file))185                    continue186                with open(ctd_cov_file) as f:187                    ctd_data = json.load(f)188                ctd_total_rows = ctd_data['test_plan_coverage_info']['test_plan_rows']189                ctd_orig_cov = round(ctd_data['test_plan_coverage_info']['rows_covered_bb_sequences'] / ctd_total_rows,190                                     2)191                ctd_final_cov = round(ctd_data['test_plan_coverage_info']['rows_covered_full'] / ctd_total_rows, 2)192                if not os.path.isdir(reports_dir):193                    print("** Warning: reports dir {} doesn't exist".format(reports_dir))194                    continue195                for comb_report_dir in os.listdir(os.path.join(reports_dir, 'jacoco-reports')):196                    if comb_report_dir.startswith('.'):197                        continue198                    comb = comb_report_dir[comb_report_dir.find('_' + str(trial) + '-') + 2 + len(str(trial)):-6]199                    if comb not in combs:200                        continue201                    # comb = comb[:comb.find('sampled') + 7] if 'sampled' in comb else comb  # remove sampling trial number202                    coverage_file = os.path.join(reports_dir, 'jacoco-reports',203                                                 comb_report_dir, comb_report_dir + '.csv')204                    if not os.path.isfile(coverage_file):205                        print("** Warning: coverage file {} doesn't exist".format(coverage_file))206                        continue207                    df_apps.append(app)208                    df_time_limits.append(time_limit)209                    df_int_levels.append(interaction_level)210                    df_combs.append(comb)211                    df_trials.append(trial)212                    df_averaging_criterion.append((app, interaction_level,213                                                comb[:comb.find('sampled') + 7] if 'sampled' in comb else comb))214                    inst, branch, method = calc_coverage(coverage_file, precision)215                    df_inst_cov.append(inst)216                    df_branch_cov.append(branch)217                    df_method_cov.append(method)218                    df_n_tests.append(count_test_methods(list(Path(os.path.join(app_dir, app_config, comb_report_dir)).rglob('*.java'))))219                    df_ctd_cov.append(ctd_final_cov if 'ctdamplified' in comb or 'ctd-amplified' in comb else ctd_orig_cov)220            cov_results = dict()221            cov_results['App'] = df_apps222            cov_results['Time limit'] = df_time_limits223            cov_results['Interaction Level'] = df_int_levels224            cov_results['Combination'] = df_combs225            cov_results['Trial'] = df_trials226            cov_results['Averaging criterion'] = df_averaging_criterion227            cov_results['Instruction coverage'] = df_inst_cov228            cov_results['Branch coverage'] = df_branch_cov229            cov_results['Method coverage'] = df_method_cov230            cov_results['Test methods'] = df_n_tests231            cov_results['CTD coverage'] = df_ctd_cov232            cov_df = pd.DataFrame(cov_results, columns=columns)233            cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate.csv'), mode='w+')234            print('Saved intermediate DataFrame to file')235        # add categorizer output to table236        if analyze_categories and categorizer_output:237            print('Reading from categorizer output')238            categories = categorizer_output[0][3:]239            print(f'Implied categories: {categories}')240            categorizer_output = categorizer_output[1:]241            cov_df[categories] = np.nan242            for cat in categorizer_output:243                # cat is ['app', 'combination', 'level', ('0', '1', 'under', 'over', 'col', 'total') * {1,2,3}]244                (cat_app, cat_comb, cat_level), cat = tuple(cat[:3]), cat[3:]245                for i in range(len(categories)):246                    cov_df.loc[(cov_df['App'] == cat_app) & (cov_df['Combination'] == cat_comb) &247                               (cov_df['Interaction Level'].astype(int) == int(cat_level)), categories[i]] = float(248                        cat[i])249            cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate2.csv'), mode='w+')250            print('Saved second intermediate DataFrame to file')251    # add three more columns252    print('Adding three more columns')253    cov_df['Instruction coverage efficiency'] \254        = np.where(cov_df['Test methods'].astype(float) > 0,255                   cov_df['Instruction coverage'].astype(float) / cov_df['Test methods'].astype(float), 0)256    cov_df['Branch coverage efficiency'] \257        = np.where(cov_df['Test methods'].astype(float) > 0,258                   cov_df['Branch coverage'].astype(float) / cov_df['Test methods'].astype(float), 0)259    cov_df['Configuration'] = cov_df['Combination'].astype(str)260    # prepare columns for samples261    sampled_cov_df = None262    if cov_df['Combination'].astype(str).str.contains('-sampled', regex=True).any():263        print(f'Detected samples -- preparing columns for samples (assuming only trial 0 may be sampled)')264        for level in levels:265            app_df = cov_df[(cov_df['App'] == app) & (cov_df['Interaction Level'].astype(int) == int(level)) & (266                        cov_df['Trial'].astype(int) == 0)]267            ctd_amplified_counts = app_df['Combination'].str.contains('ctd-amplified-sampled',268                                                                      regex=True).value_counts()269            evosuite_counts = app_df['Combination'].str.contains('evosuite-sampled', regex=True).value_counts()270            randoop_counts = app_df['Combination'].str.contains('randoop-sampled', regex=True).value_counts()271            sampled_suites = [True in ctd_amplified_counts, True in evosuite_counts, True in randoop_counts]272            if sampled_suites.count(True) != 2:273                continue274            non_sampled = \275                ['ctd-amplified', 'evosuite', 'randoop'][sampled_suites.index(False)]276            if non_sampled != 'ctd-amplified':277                implied_sampling_trials = ctd_amplified_counts[True]278            else:279                implied_sampling_trials = evosuite_counts[True]280                assert implied_sampling_trials == randoop_counts[True]281            print(f'Duplicating {non_sampled}, {implied_sampling_trials} times (app={app}, level={level}, trial=0)')282            not_sampled_rows = app_df[app_df['Combination'].str.match(f'{non_sampled}$')]283            not_sampled_rows.replace(non_sampled, f'{non_sampled}-sampled', inplace=True)284            cov_df = cov_df.append([not_sampled_rows] * implied_sampling_trials)285        cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_intermediate3.csv'), mode='w+')286        print('Saved non-loadable third intermediate DataFrame to file')287        sampled_cov_df = cov_df[cov_df['Combination'].str.contains('-sampled', regex=True)]288        sampled_cov_df.replace('-sampled(-[0-9]+)?', ' (sampled)', regex=True, inplace=True)289        sampled_cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_sampled.csv'), mode='w+')290    cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results.csv'), mode='w+')291    cov_df = cov_df[~cov_df['Combination'].str.contains('-sampled', regex=True)]292    sing_cov_df = cov_df[cov_df['Combination'].isin(['ctd-amplified', 'evosuite', 'randoop'])]293    sing_cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_singles.csv'), mode='w+')294    comb_cov_df = cov_df[cov_df['Combination'].isin(['ctdamplified-evosuite-randoop', 'evosuite-randoop'])]295    comb_cov_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_combinations.csv'), mode='w+')296    # take mean over all instances of same config (dropping sampling trial number must be done after cat analysis)297    print('Averaging DataFrame values according to averaging criterion')298    def take_avg(col):299        return str(col.mean()) if is_numeric_dtype(col) else col.unique() if col.nunique() == 1 else np.NaN300    sampled_avg_df = None301    if sampled_cov_df is not None:302        sampled_avg_df = sampled_cov_df.groupby('Averaging criterion').agg(take_avg)303        sampled_avg_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_sampled_averaged.csv'), mode='w+')304    sing_avg_df = sing_cov_df.groupby('Averaging criterion').agg(take_avg)305    sing_avg_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_singles_averaged.csv'), mode='w+')306    comb_avg_df = comb_cov_df.groupby('Averaging criterion').agg(take_avg)307    comb_avg_df.to_csv(os.path.join(app_dir, f'{app}_cov_analysis_results_combinations_averaged.csv'), mode='w+')308    # plot graphs based on raw data309    images_folder = os.path.join(output_dir_abs, images_dir)310    Path(images_folder).mkdir(exist_ok=True)311    avg_dfs = [sing_avg_df, comb_avg_df] + ([sampled_avg_df] if sampled_avg_df is not None else [])312    all_dfs = [(sing_cov_df, 'singles'), (comb_cov_df, 'combinations')] \313        + ([(sampled_cov_df, 'sampled')] if sampled_cov_df is not None else [])314    if plots_per_app:315        for avg_df, (_, name_df) in zip(avg_dfs, all_dfs):316            if avg_df is not None:317                print_app_df([app], avg_df, name_df, images_folder, categories, rng_y)318def run_coverage_analysis(config, categorizer_output):319    """The main entry point for analyzing experiments coverage results.320    This is the main entry point for an experiment coverage analysis. For each app in the experiment,321    each interaction level, each time limit, and each combination of resulting test suites (out of322    randoop, evosuite, and ctd-guided generated test suites), it computes the average instruction, branch323    and method coverage, test suite size (in terms of test methods) and *false* ctd coverage (in terms of rows covered324    out of the ctd test plan)325    """326    for app in config['general']['apps']:327        run_coverage_analysis_separate(app, config, categorizer_output)328def parse_oc_json(file_name, pts, pts2, verbose=False, app=''):329    with open(file_name) as f:330        oc_methods = json.load(f)331    level = oc_methods['interaction_level']332    oc_methods = oc_methods['ctd_coverage']['monolithic']333    for cls in oc_methods:334        methods = oc_methods[cls]335        for name in methods:336            ctd_cov = float(methods[name]['ctd_coverage'])337            ctd_cov_bb = float(methods[name]['ctd_coverage_bb_sequences'])338            pts.append({'Configuration': 'ctd-amplified', 'Combination': 'ctd-amplified', 'Interaction Level': level, 'CTD Coverage': ctd_cov, 'Filename': file_name})339            pts.append({'Configuration': 'evosuite-randoop', 'Combination': 'ctd-amplified', 'Interaction Level': level, 'CTD Coverage': ctd_cov_bb, 'Filename': file_name})340            pts2.append({'Class': cls, 'Method': name, 'Interaction Level': level, 'Coverage by CTD-Amplified': ctd_cov, 'Coverage by Basic Block': ctd_cov_bb, 'App': app, 'Filename': file_name})341            if verbose:342                print(f'OC level:{level} appended {ctd_cov} vs. {ctd_cov_bb}')343def parse_all_oc(oc_apps, time_limit, trial, output_dir, verbose=False):344    pts, pts2 = [], []345    if not oc_apps:346        json_glob = Path(output_dir).rglob(f'*/*_{time_limit}s_*l_{trial}/*_ctd_coverage_report.json')347        for file_name in json_glob:348            parse_oc_json(file_name, pts, pts2, verbose)349            if verbose:350                print(f'Finished parsing json file {file_name} for oc')351    else:352        for app in oc_apps:353            app_json_glob = Path(output_dir).rglob(f'{app}/{app}_{time_limit}s_*l_{trial}/*_ctd_coverage_report.json')354            for file_name in app_json_glob:355                parse_oc_json(file_name, pts, pts2, verbose, app)356                if verbose:357                    print(f'Finished parsing json file {file_name} for oc')358    return pts, pts2359def plot_oc(time_limit, trial, config, csv_file=None):360    apps, output_dir = config['general']['apps'], config['general']['output_dir']361    verbose = config['verbosity']['verbose']362    images_dir, pts, rng_y = config['analyze']['images_dir'], config['analyze']['pts'], config['analyze']['rng_y']363    if not csv_file:364        pts, pts2 = parse_all_oc(apps, time_limit, trial, verbose)365        pts_df = pd.DataFrame(pts)366        pts2_df = pd.DataFrame(pts2)367        pts2_df.to_csv(os.path.join(output_dir, f'ctd_coverage_analysis_oc2_{time_limit}s_{trial}.csv'))368    else:369        f = open(csv_file)370        pts_df = pd.read_csv(f)371        pts_df.columns = ['0'] + ['Configuration', 'Combination', 'Interaction Level', 'CTD Coverage', 'Filename']372        pts_df.drop('0', axis=1, inplace=True)373    current_images_folder_base = os.path.join(output_dir, images_dir, 'ctd-coverage')374    current_images_folder = os.path.join(current_images_folder_base, 'all-apps')375    Path(current_images_folder).mkdir(parents=True, exist_ok=True)376    pts_df.to_csv(os.path.join(output_dir, f'ctd_coverage_analysis_oc_{time_limit}s_{trial}.csv'))377    print(f'Written to file {output_dir}/tkl_coverage_analysis_oc_{time_limit}s_{trial}.csv')378    print(f'Written to auxiliary file {output_dir}/tkl_coverage_analysis_oc2_{time_limit}s_{trial}.csv')379    print('Plotting *true* ctd coverage graph for all data')380    if not pts_df.empty:381        column = 'CTD Coverage'382        pts_df.sort_values(by=['Interaction Level', 'Combination'], axis=0, inplace=True)383        fig = px.box(pts_df, x='Interaction Level', y=column, color='Configuration', points=pts,384                     width=720, range_y=rng_y).update_traces(boxmean=True).update_layout(385            legend=dict(386                orientation="h",387                yanchor="bottom",388                y=1.02,389                xanchor="right",390                x=1391            ),392        )393        fig.write_image(os.path.join(current_images_folder, f'{snake_case(column, pre="true")}.png'), format='png')394        print('Written image ' + os.path.join(current_images_folder, f'{snake_case(column, pre="true")}.png'))395def parse_exceptions_json(file_name, app, time_limit, level, trial, exceptions_csv, verbose=False):396    with open(file_name) as f:397        summary = json.load(f)398    if 'execution_fail_exception_types' not in summary.keys():399        return exceptions_csv400    exceptions = summary['execution_fail_exception_types']401    for exception in exceptions:402        ex_count = exceptions[exception]403        exception = str(exception).split()[0].split('.')[-1]404        exceptions_csv.append([app, 'ctd-amplified', time_limit, level, trial, exception, ex_count])405        if verbose:406            print(f'Appended {exception} ({ex_count})')407    return exceptions_csv408def parse_exceptions(time_limit, level, trial, config):409    apps, output_dir = config['general']['apps'], config['general']['output_dir']410    verbose = config['verbosity']['verbose']411    from csv import reader, writer412    with open(f'{output_dir}/exceptions_summary.csv', 'r') as read_obj:413        csv_reader = reader(read_obj)414        exceptions_csv = list(csv_reader)415    real_apps = []416    for app in apps:417        if os.path.exists(f'{output_dir}/{app}/{app}_{time_limit}s_{level}l_{trial}/{app}_{time_limit}s_{level}l_{trial}_test_generation_summary.json'):418            real_apps.append(app)419    print(f'Real apps: {real_apps} ({len(real_apps)} out of {len(apps)})')420    for app in real_apps:421        parse_exceptions_json(f'{output_dir}/{app}/{app}_{time_limit}s_{level}l_{trial}/{app}_{time_limit}s_{level}l_{trial}_test_generation_summary.json',422                              app, time_limit, level, trial, exceptions_csv, verbose)423        if verbose:424            print(f'Finished parsing test-gen summary json for app {app} for exceptions')425    with open(f'{output_dir}/tkl_coverage_analysis_ex_{time_limit}s_{level}l_{trial}_raw.csv', 'w') as write_obj:426        csv_writer = writer(write_obj)427        csv_writer.writerows(exceptions_csv)428        print(f'Written to file {output_dir}/tkl_coverage_analysis_ex_{time_limit}s_{level}l_{trial}_raw.csv')429    exceptions_df = pd.DataFrame(exceptions_csv, columns=['App', 'Configuration', 'Timeout', 'Interaction Level', 'Trial', 'Exception', 'Count'])430    exceptions_df.sort_values(['App', 'Configuration', 'Exception'], inplace=True)431    exceptions_df.to_csv(f'{output_dir}/tkl_coverage_analysis_ex_{time_limit}s_{level}l_{trial}.csv')432    print(f'Written to file {output_dir}/tkl_coverage_analysis_ex_{time_limit}s_{level}l_{trial}.csv')433    plot_exceptions(time_limit, level, trial, output_dir, exceptions_df=exceptions_df)434def plot_exceptions(time_limit, level, trial, config, exceptions_df=None, csv_file=None):435    output_dir = config['general']['output_dir']436    images_dir = config['analyze']['images_dir']437    if exceptions_df is None:438        open_csv(csv_file, columns=['App', 'Configuration', 'Timeout', 'Interaction Level', 'Trial', 'Exception', 'Count'], categorized=False)439    for conf in ['ctd-amplified', 'evosuite', 'randoop']:440        exceptions_df.loc[(exceptions_df['Configuration'] == conf), 'Percentage'] = \441            exceptions_df[(exceptions_df['Configuration'] == conf)]['Count'] / \442            exceptions_df[(exceptions_df['Configuration'] == conf)]['Count'].sum()443    for conf in ['ctd-amplified', 'evosuite', 'randoop']:444        conf_df = exceptions_df[(exceptions_df['Configuration'] == conf) & (exceptions_df['Timeout'].astype(int) == time_limit) &445                                (exceptions_df['Interaction Level'].astype(int) == level) & (exceptions_df['Trial'].astype(int) == trial)].copy()446        if conf_df.empty:447            continue448        conf_df.sort_values('Exception', ascending=False, inplace=True)449        bars = px.bar(conf_df,450                      y='Exception', x='Percentage',451                      title=f'Histogram of exceptions for configuration {conf} and level {level}',452                      orientation='h',453                      width=1080, height=1720).update_layout(454            yaxis=dict(tickmode='linear'),455        )456        bars.write_image(os.path.join(output_dir, images_dir, f'exceptions_histogram_{conf}_{time_limit}s_{level}l_{trial}.png'), format='png')457        print(f"Written histogram image {os.path.join(output_dir, images_dir, f'exceptions_histogram_{conf}_{time_limit}s_{level}l_{trial}.png')}")458    exceptions_df.sort_values(['App', 'Configuration', 'Exception'], inplace=True)459    exceptions_df.sort_values('Exception', ascending=False, inplace=True)460    rare_exceptions = []461    rare_threshold = 0.02462    for exception in list(exceptions_df['Exception']):463        percent = {conf: exceptions_df[(exceptions_df['Exception'] == exception) &464                                       (exceptions_df['Configuration'] == conf)]['Percentage'].sum()465                   for conf in ['ctd-amplified', 'evosuite', 'randoop']}466        if percent['ctd-amplified'] <= rare_threshold and percent['evosuite'] <= rare_threshold and percent['randoop'] <= rare_threshold:467            rare_exceptions.append(exception)468        else:469            if exception[-1] == ';':470                exception_new = exception[:-1]471                exceptions_df['Exception'].replace(exception, exception_new, regex=False, inplace=True)472            if exception != 'Exception':473                exception_new = re.sub('Exception', '', exception)474                exceptions_df['Exception'].replace(exception, exception_new, regex=False, inplace=True)475    print('Rare exceptions:', rare_exceptions)476    exceptions_df = exceptions_df[~exceptions_df['Exception'].isin(rare_exceptions)]477    exceptions_df.sort_values(['Exception', 'Configuration'], ascending=[False, True], inplace=True)478    exceptions_df.replace('ctd-amplified', 'ctd-guided', inplace=True)479    bars = px.bar(exceptions_df,480                  y='Exception', x='Percentage', color='Configuration',481                  # title=f'Histogram of exceptions per configuration for level {level}',482                  orientation='h', width=1080, height=1320).update_layout(483        yaxis=dict(tickmode='linear'), yaxis_title=None,484        xaxis=dict(tick0=0, dtick=0.05, tickformat=',.0%'), xaxis_title=None,485        font=dict(size=27),486        legend=dict(orientation="h", yanchor="bottom", y=1.002, xanchor="right", x=1),487        legend_title_text='',488        barmode='group',489    )490    bars.write_image(os.path.join(output_dir, images_dir, f'exceptions_histogram_all_{time_limit}s_{level}l_{trial}.png'), format='png')491    print(f"Written histogram image {os.path.join(output_dir, images_dir, f'exceptions_histogram_all_{time_limit}s_{level}l_{trial}.png')}")492def main_cumulate(config):493    columns = COLUMNS494    output_dir_abs = os.path.abspath(config['general']['output_dir'])495    images_folder = os.path.join(output_dir_abs, config['analyze']['images_dir'])496    for name_csv in ['singles', 'combinations', 'sampled']:497        all_csvs = []498        for app in config['general']['apps']:499            app_dir = os.path.join(output_dir_abs, app)500            all_csvs.append(os.path.join(app_dir, f'{app}_cov_analysis_results_{snake_case(name_csv)}.csv'))501        print_all_csv_cumulate(all_csvs, name_csv, columns, images_folder, config)502if __name__ == '__main__':503    apps = sys.argv[1:]504    with open('parsed_config.toml') as f:505        config = toml.load(f)506    if apps:507        # parse_exceptions(60, 3, 0, config)508        plot_exceptions(60, 3, 0, config, csv_file='/Users/antonio/Desktop/FINAL/exceptions/tkl_coverage_analysis_ex_60s_3l_0.csv')...

web.py

Source:web.py

...272                }273            )274        @app.route("/exceptions/csv")275        @self.auth_required_if_enabled276        def exceptions_csv():277            data = StringIO()278            writer = csv.writer(data)279            self.stats_csv_writer.exceptions_csv(writer)280            return _download_csv_response(data.getvalue(), "exceptions")281    def start(self):282        self.greenlet = gevent.spawn(self.start_server)283        self.greenlet.link_exception(greenlet_exception_handler)284    def start_server(self):285        if self.tls_cert and self.tls_key:286            self.server = pywsgi.WSGIServer(287                (self.host, self.port), self.app, log=None, keyfile=self.tls_key, certfile=self.tls_cert288            )289        else:290            self.server = pywsgi.WSGIServer((self.host, self.port), self.app, log=None)291        self.server.serve_forever()292    def stop(self):293        """...

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