debugging_actions.py

import os
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt


def parse_coding_framework_csv(file):
    merged_array = []
    p_name = file.split(' ')[-1][:-4]
    tmp_df = pd.read_csv(file, delimiter=',', skipinitialspace=True)

    strategies = tmp_df['Strategies'].tolist()
    strategies = pd.DataFrame(strategies, columns=['strat']).ffill()['strat'].to_list()

    actions = tmp_df['Actions'].tolist()
    timeframes = list(tmp_df)[2:]
    # print(timeframes)
    for tf in timeframes:
        data = tmp_df[tf].tolist()
        for strat, act, dat in zip(strategies, actions, data):
            if not pd.isna(dat):
                merged_array.append([p_name, tf, strat, act, dat])
    return pd.DataFrame(merged_array, columns=['Participant', 'Timeframe', 'Strategy', 'Action', 'Marker'])


def parse_data(path_to_data):
    participants_files = [os.path.join(path_to_data, file) for file in
                          os.listdir(path_to_data)]

    dfs = []
    for file in participants_files:
        dfs.append(parse_coding_framework_csv(file))
    df = pd.concat(dfs).reset_index(drop=True)

    # Filter
    strategies_to_exclude = ['No code', 'Bug found', 'Bug not found', 'No strategy', 'No strategy ',
                             'Hilfestellung']
    df = df[~df['Strategy'].isin(strategies_to_exclude)]

    # change strategy names to those from the current version of the paper
    # ['App Actions' 'Code inspection' 'Information Needs' 'Intuition' 'Planning'
    #  'Problemformulierung' 'Scientific strategy' 'Sensemaking' 'controlflow following'
    #  'dataflow following' 'Simulation']
    strategy_name_mapping = {
        'App Actions': 'Spatial memory',
        'Code inspection': 'Inspect source code',
        'Information Needs': 'Program comprehension',
        'Intuition': 'Intuition',
        'Planning': 'Planning',
        'Problemformulierung': 'Scientific strategy',
        'Scientific strategy': 'Scientific strategy',
        'Sensemaking': 'Scientific strategy',
        'controlflow following': 'Control flow following',
        'dataflow following': 'Data flow following',
        'Simulation': 'Simulation'
    }
    df['Strategy'] = df['Strategy'].map(strategy_name_mapping)

    to_replace = {'Cause-effect chain starts': None,
                  'Changes location in code/graph because of a method-call': "follow control flow",
                  'Changes location in code/graph because of a variable': "follow data flow",
                  'Configuration': "configuration",
                  'Hotspot': "hotspot",
                  'Open Codewindow (from Code Window)': 'open window',
                  'Open Codewindow (from Graph)': 'open window',
                  'Open Method inside window (click on graph)': "follow control flow",
                  'Open Method inside window (click on window)': "follow control flow",
                  'P ist lost und braucht Hilfe': 'participants want help',
                  'Player Movement': 'move player',
                  'Reposition Windows': 'move window',
                  'accept hypothesis': "accept/reject hypothesis",
                  'browsing/scrolling/scanning': "scanning",
                  'deducting from prior knowledge': None,
                  'expresses a plan or series of tasks that they want to do': "planning",
                  'expresses insight/statement/assumption\nbased on a feeling/experience/intuition': "follow intuition",
                  'plan successfully executed': None,
                  'reject hypothesis': "accept/reject hypothesis",
                  'specific code area, detailed reading': "code reading",
                  'state & test hypothesis': "state/test hypothesis",
                  'inducting from code bases': None,
                  'Bug report': "bug report",
                  'Close Codewindow': 'close window',
                  'problem formulation': None,
                  'browsing/scrolling/scanning\n(attention on code window)': "scanning",
                  'simulates execution': "simulate execution",
                  'browsing/scrolling/scanning\n(attention on code window)\n(some expression about source code)': "scanning",
                  'Cause-effect chain end': None}

    df["Action"] = df["Action"].replace(to_replace)

    strategy_agg_df = df.groupby(['Participant', 'Strategy'])['Marker'].agg('count').reset_index()
    strategy_agg_df['Marker'] = strategy_agg_df['Marker'] * 4 / 60
    return df, strategy_agg_df


def plot_debugging_actions_overview(df, out_path, order, xticklabels):
    hue_order = ["P1", "P2", "P3", "P4", "P5", "P6", "P7", "P8", "P9", "P10", "P11", "P12"]

    cols = ['#a50026', '#d73027', '#f46d43', '#fdae61', '#fee090', '#ffffbf', '#e0f3f8', '#abd9e9'
            , '#74add1', '#4575b4', '#313695', '#11155d']

    plt.figure(figsize=(10, 4))

    g = sns.barplot(x="Strategy", y="Marker", hue='Participant', data=df,
                    errorbar=None, hue_order=hue_order, palette=cols, order=order)

    plt.xlabel('Debugging Actions')
    plt.ylabel('Time (min)')
    g.set_xticklabels(xticklabels)

    plt.savefig(os.path.join(out_path, 'Debugging Actions Overview.png'), bbox_inches='tight')


def plot_debugging_actions_overview_agg(df, out_path, order, xticklabels):
    cols = ['#f46d43', '#4575b4']

    strategy_overview_sum = df.groupby('Strategy')['Marker'].sum()
    strategy_overview_avg = df.groupby('Strategy')['Marker'].mean()

    strats = list(strategy_overview_sum.index.values)

    out = []
    for strat, so_sum, so_avg in zip(strats, strategy_overview_sum, strategy_overview_avg):
        out.append([strat, 'Average', so_avg])
        out.append([strat, 'Sum', so_sum])

    strategy_overview = pd.DataFrame(data=out, columns=['Strategy', 'Type', 'Value'])

    plt.figure(figsize=(7, 2))

    g = sns.barplot(x="Strategy", y="Value", hue="Type", data=strategy_overview,
                    errorbar=None, order=order, palette=cols)

    plt.xlabel('Debugging Actions Aggregated')
    plt.ylabel('Time (min)')
    g.set_xticklabels(xticklabels)
    g.get_legend().set_title(None)

    plt.savefig(os.path.join(out_path, 'Debugging Actions Overview Agg.png'), bbox_inches='tight')


def extract_debugging_actions():
    path_to_data = './data/user study/debugging actions data'
    out_path = './output'
    df, agg_df = parse_data(path_to_data)

    # program comprehension (PC), inspect source code (InSC), planning (Pl), simulation (Si), scientific strategy (Sci),
    # intuition (Int), data flow following (DFF), control flow following (CFF), and spatial memory (SM)
    xticklabels = ['PC', 'InSC', 'Pl', 'Si', 'Sci', 'Int', 'DFF', 'CFF', 'SM', ]
    order = ['Program comprehension', 'Inspect source code', 'Planning', 'Simulation', 'Scientific strategy',
             'Intuition', 'Data flow following', 'Control flow following', 'Spatial memory']

    plot_debugging_actions_overview(agg_df, out_path, order, xticklabels)
    plot_debugging_actions_overview_agg(agg_df, out_path, order, xticklabels)
    return