1600 lines
627 KiB
Plaintext
1600 lines
627 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from cliffs_delta import cliffs_delta\n",
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"from scipy.stats import mannwhitneyu, chi2_contingency"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 软件生态名\n",
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"ECO_NAMES = [\n",
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" # \"Apache\",\n",
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" # \"Jira\",\n",
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" # \"Mojang\",\n",
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" # \"MongoDB\",\n",
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" # \"Qt\",\n",
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" \"RedHat\",\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"PRO_ISSUE_DIR = Path(\"../data/processed/issues\")\n",
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"PRO_LINK_DIR = Path(\"../data/processed/links\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"RQ1_RESULT_DIR = Path(\"../data/rq1\")\n",
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"RQ1_RESULT_DIR.mkdir(parents=True, exist_ok=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 0.加载数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_df(eco_name: str):\n",
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" # 加载Issue和链接数据DataFrame\n",
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"\n",
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" issue_file = PRO_ISSUE_DIR / (eco_name + \".csv\")\n",
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" issue_df = pd.read_csv(\n",
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" issue_file,\n",
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" sep=\";\",\n",
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" parse_dates=[\"created_time\", \"closed_time\"],\n",
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" encoding=\"utf-8\",\n",
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" low_memory=False,\n",
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" )\n",
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"\n",
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" link_file = PRO_LINK_DIR / (eco_name + \".csv\")\n",
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" link_df = pd.read_csv(\n",
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" link_file,\n",
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" sep=\";\",\n",
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" parse_dates=[\n",
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" \"link_created_time\",\n",
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" \"created_time_in\",\n",
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" \"created_time_out\",\n",
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" \"closed_time_in\",\n",
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" \"closed_time_out\",\n",
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" ],\n",
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" encoding=\"utf-8\",\n",
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" low_memory=False,\n",
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" )\n",
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"\n",
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" return issue_df, link_df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1.基本信息"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def add_link_scope(link_df: pd.DataFrame):\n",
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" # 根据链接两端项目关键字确定链接范围\n",
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"\n",
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" link_df[\"scope\"] = np.where(\n",
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" link_df[\"project_key_in\"] == link_df[\"project_key_out\"],\n",
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" \"in\",\n",
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" \"cross\",\n",
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" )\n",
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"\n",
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" return link_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_overview(eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame):\n",
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" # 统计生态的基本信息\n",
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"\n",
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" # 取出所有Issue和有链接的Issue\n",
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" issue_set = set(issue_df[\"key\"])\n",
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" issues_wlink = pd.concat([link_df[\"in_issue_key\"], link_df[\"out_issue_key\"]])\n",
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" issue_set_wlink = set(issues_wlink)\n",
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" # Issue总数、有链接的Issue总数、链接总数\n",
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" num_issues = len(issue_set)\n",
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" num_issues_wlink = len(issue_set_wlink)\n",
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" num_links = len(link_df)\n",
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"\n",
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" # 有链接的Issue所占比例\n",
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" ratio_issues_wlink = round(num_issues_wlink / num_issues * 100, 2)\n",
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"\n",
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" # 计算有链接Issue的链接数\n",
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" issues_wlink_counts = issues_wlink.value_counts()\n",
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" # 最大、最小、中位、平均链接数\n",
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" max_links = issues_wlink_counts.max()\n",
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" min_links = issues_wlink_counts.min()\n",
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" median_links = issues_wlink_counts.median()\n",
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" mean_links = issues_wlink_counts.mean()\n",
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"\n",
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" # 计算不同链接数范围对应的Issue数目及比例\n",
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" # 定义范围\n",
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" bins = [0, 2, 5, 10, float(\"inf\")] # 使用float('inf')表示无穷大\n",
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" labels = [\"<=2\", \"3-5\", \"6-10\", \">10\"]\n",
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" # 将issues_wlink_counts分组到定义的范围内\n",
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" count_ranges = pd.cut(issues_wlink_counts, bins=bins, labels=labels)\n",
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" # 计算每个范围内的Issue数目（降序排列）及比例（占有链接的Issue比例）\n",
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" count_dist = count_ranges.value_counts().sort_values(ascending=False)\n",
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" count_proportions = count_dist / num_issues_wlink\n",
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"\n",
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" # 绘制图像并保存为pdf文件\n",
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" count_dist.plot(kind=\"bar\", figsize=(8, 6), logy=True, color=\"#445766\")\n",
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" plt.title(\"Link count distribution\")\n",
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" plt.xlabel(\"Link count bins\")\n",
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" plt.ylabel(\"Issue count with proportion\")\n",
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" plt.xticks(rotation=45, ha=\"right\") # 旋转x轴标签并右对齐以改善可读性\n",
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" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
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" # 为每个条形添加文本标签\n",
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" for i, value in enumerate(count_dist):\n",
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" text = f\"{value} ({count_proportions.iloc[i]:.2%})\" # 将比例格式化为百分比\n",
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" plt.text(i, value, text, ha=\"center\", va=\"bottom\")\n",
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" # 保存为PDF\n",
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" plt.savefig(RQ1_RESULT_DIR / \"link_count_dist.pdf\", format=\"pdf\")\n",
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" # plt.show()\n",
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"\n",
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" # 绘制前30链接数最多的Issue\n",
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" issues_wlink_counts.head(30).plot(kind=\"bar\", figsize=(15, 5), color=\"#445766\")\n",
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" plt.xlabel(\"Issue\")\n",
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" plt.ylabel(\"#Links\")\n",
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" plt.title(\"The Top 30 Issues with the Most Links\")\n",
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" plt.xticks(rotation=45, ha=\"right\") # 旋转x轴标签并右对齐以改善可读性\n",
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" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
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" # 保存为PDF\n",
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" plt.savefig(RQ1_RESULT_DIR / \"top30_issues_with_links.pdf\", format=\"pdf\")\n",
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" # plt.show()\n",
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"\n",
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" # 链接类别数与项目总数\n",
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" num_link_types = len(link_df[\"link_type\"].unique())\n",
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" num_projects = len(issue_df[\"project_key\"].unique())\n",
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"\n",
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" # 跨项目链接比例\n",
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" num_cross_project = link_df[\"scope\"].value_counts().get(\"cross\", 0)\n",
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" ratio_cross_project = round(num_cross_project / num_links * 100, 2)\n",
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"\n",
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" return (\n",
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" eco_name,\n",
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" num_issues,\n",
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" num_issues_wlink,\n",
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" num_links,\n",
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" ratio_issues_wlink,\n",
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" max_links,\n",
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" min_links,\n",
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" median_links,\n",
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" mean_links,\n",
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" num_link_types,\n",
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" num_projects,\n",
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" num_cross_project,\n",
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" ratio_cross_project,\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 基本信息\n",
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"overview = pd.DataFrame(\n",
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" columns=[\n",
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" \"ecosystem\", # 生态名\n",
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" \"#issues\", # Issue总数\n",
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" \"#issues_with_links\", # 有链接的Issue总数\n",
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" \"#links\", # 链接总数\n",
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" \"%issues_with_links\", # 有链接的Issue所占比例\n",
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" \"#max_links\", # 最大链接数\n",
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" \"#min_links\", # 最小链接数\n",
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" \"#median_links\", # 链接中位数\n",
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" \"#mean_links\", # 平均链接数（即总链接数除以有链接的Issue总数）\n",
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" \"#link_types\", # 链接类别数\n",
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" \"#projects\", # 生态内项目总数\n",
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" \"#links_cross_project\", # 跨项目链接总数\n",
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" \"%links_cross_project\", # 跨项目链接比例\n",
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" ],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2.Issue创建、链接建立时间差"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_time_interval(eco_name: str, link_df: pd.DataFrame):\n",
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" # 统计分析两类时间差\n",
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"\n",
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" def get_time_stats(scope: str, interval_type: str, df: pd.DataFrame):\n",
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"\n",
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" # 取出对应范围的链接\n",
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" if scope != \"with\":\n",
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" df = df[df[\"scope\"] == scope]\n",
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"\n",
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" # 计算各个统计指标\n",
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" max_interval = df[interval_type].max().days\n",
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" min_interval = df[interval_type].min().days\n",
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" median_interval = df[interval_type].median().days\n",
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" mean_interval = df[interval_type].mean().days\n",
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" std_interval = df[interval_type].std().days\n",
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"\n",
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" return (\n",
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" eco_name,\n",
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" scope,\n",
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" interval_type,\n",
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" max_interval,\n",
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" min_interval,\n",
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" median_interval,\n",
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" mean_interval,\n",
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" std_interval,\n",
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" )\n",
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"\n",
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" def get_wmw_stats(\n",
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" first_val: str,\n",
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" second_val: str,\n",
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" interval_type: str, # 时间间隔类型\n",
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" df: pd.DataFrame,\n",
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" alternative: str = \"two-sided\", # 假设类型\n",
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" ):\n",
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" # 检验first_val的interval_type的分布是否小于seconde_val\n",
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" first_df = df[df[\"scope\"] == first_val]\n",
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" second_df = df[df[\"scope\"] == second_val]\n",
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" # 执行WMW检验\n",
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" u_stat, p_value = mannwhitneyu(\n",
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" first_df[interval_type], second_df[interval_type], alternative=alternative\n",
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" )\n",
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" return p_value\n",
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"\n",
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" def get_cliff_delta(\n",
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" first_val: str,\n",
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" second_val: str,\n",
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" interval_type: str, # 时间间隔类型\n",
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" df: pd.DataFrame,\n",
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" ):\n",
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" first_df = df[df[\"scope\"] == first_val]\n",
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" second_df = df[df[\"scope\"] == second_val]\n",
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" # 计算Cliff Delta效应值\n",
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" delta, res = cliffs_delta(\n",
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" first_df[interval_type].dt.total_seconds(),\n",
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" second_df[interval_type].dt.total_seconds(),\n",
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" )\n",
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" return delta, res\n",
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"\n",
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" # 裁切出使用到的列\n",
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" link_df = link_df[\n",
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" [\n",
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" \"link_type\",\n",
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" \"in_issue_key\",\n",
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" \"out_issue_key\",\n",
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" \"link_created_time\",\n",
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" \"created_time_in\",\n",
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" \"created_time_out\",\n",
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" \"scope\",\n",
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" ]\n",
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" ]\n",
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"\n",
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" # 删除掉link_created_time、created_time_in、created_time_out为空的行\n",
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" link_df = link_df.dropna(\n",
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" subset=[\"link_created_time\", \"created_time_in\", \"created_time_out\"], how=\"any\"\n",
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" )\n",
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" print(\n",
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" f\"✔ {len(link_df)} links after removing links with null link/issue created time\"\n",
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" )\n",
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"\n",
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" # 创建CTI、LTI两列\n",
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" link_df[\"cti\"] = (link_df[\"created_time_in\"] - link_df[\"created_time_out\"]).abs()\n",
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" max_created_time = link_df[[\"created_time_in\", \"created_time_out\"]].max(axis=1)\n",
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" link_df[\"lti\"] = link_df[\"link_created_time\"] - max_created_time\n",
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" # !注意：有些行的LTI值为负，这是因为获取link_created_time时出错，直接删除对应行\n",
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" link_df = link_df[link_df[\"lti\"] >= pd.Timedelta(0)]\n",
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" print(f\"✔ {len(link_df)} links after removing links with negative LTI\")\n",
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"\n",
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" # 统计CTI、LTI两列\n",
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" time_interval_df = pd.DataFrame(\n",
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" columns=[\n",
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" \"ecosystem\", # 生态名\n",
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" \"scope\", # 链接范围\n",
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" \"interval_type\", # 时间间隔类型\n",
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" \"max\", # 最大值\n",
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" \"min\", # 最小值\n",
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" \"median\", # 中值\n",
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" \"mean\", # 平均值\n",
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" \"std\", # 标准差\n",
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" ],\n",
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" )\n",
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"\n",
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" for scope in [\"with\"] + list(link_df[\"scope\"].unique()):\n",
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" for interval_type in [\"cti\", \"lti\"]:\n",
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" time_interval_df.loc[len(time_interval_df)] = get_time_stats(\n",
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" scope, interval_type, link_df\n",
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" )\n",
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"\n",
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" # 执行WMW假设检验\n",
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" for interval_type in [\"cti\", \"lti\"]:\n",
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" first_val, second_val = \"in\", \"cross\"\n",
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" p_value_ne = get_wmw_stats(first_val, second_val, interval_type, link_df)\n",
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" p_value_le = get_wmw_stats(\n",
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" first_val, second_val, interval_type, link_df, \"less\"\n",
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" )\n",
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" p_value_ge = get_wmw_stats(\n",
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" first_val, second_val, interval_type, link_df, \"greater\"\n",
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" )\n",
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" delta, res = get_cliff_delta(first_val, second_val, interval_type, link_df)\n",
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"\n",
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" print(f\"{interval_type.upper()}\")\n",
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" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
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" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
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" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
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" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
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"\n",
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" # 定义特定时间刻度（秒）\n",
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" times_in_seconds = {\n",
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" \"1 minute\": 60,\n",
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" \"1 hour\": 3600,\n",
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" \"1 day\": 86400,\n",
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" \"1 month\": 86400 * 30,\n",
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" \"1 year\": 86400 * 365,\n",
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" }\n",
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" for interval_type in [\"cti\", \"lti\"]:\n",
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" # 转换 timedelta 为秒数，并加上偏移量，避免对0做对数变换\n",
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" offset = 0\n",
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" link_df[interval_type + \"_secs\"] = (\n",
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" link_df[interval_type].dt.total_seconds() + offset\n",
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" )\n",
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"\n",
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" sns.set_theme(style=\"whitegrid\")\n",
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" plt.figure(figsize=(10, 6))\n",
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"\n",
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" # 绘制小提琴图\n",
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" # sns.violinplot(\n",
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" # x=\"scope\", y=(interval_type + \"_secs\"), data=link_df, scale=\"width\"\n",
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" # )\n",
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"\n",
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" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=link_df,\n",
|
||
" x=\"scope\",\n",
|
||
" y=(interval_type + \"_secs\"),\n",
|
||
" palette=\"pastel\",\n",
|
||
" width=0.5,\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{interval_type.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{interval_type.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" plt.savefig(RQ1_RESULT_DIR / (interval_type + \"_dist.pdf\"), format=\"pdf\")\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return time_interval_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"time_interval = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 一般类型链接（排除Epic、Subtask）CTI、LTI"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_time_interval_gen(eco_name: str, link_df: pd.DataFrame):\n",
|
||
" # 统计分析一般类型两类时间差\n",
|
||
"\n",
|
||
" print(\"❕ Without Epic and Subtask Links\")\n",
|
||
"\n",
|
||
" def get_time_stats(scope: str, interval_type: str, df: pd.DataFrame):\n",
|
||
" # 取出对应范围的链接\n",
|
||
" if scope != \"with\":\n",
|
||
" df = df[df[\"scope\"] == scope]\n",
|
||
"\n",
|
||
" # 计算各个统计指标\n",
|
||
" max_interval = df[interval_type].max().days\n",
|
||
" min_interval = df[interval_type].min().days\n",
|
||
" median_interval = df[interval_type].median().days\n",
|
||
" mean_interval = df[interval_type].mean().days\n",
|
||
" std_interval = df[interval_type].std().days\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" scope,\n",
|
||
" interval_type,\n",
|
||
" max_interval,\n",
|
||
" min_interval,\n",
|
||
" median_interval,\n",
|
||
" mean_interval,\n",
|
||
" std_interval,\n",
|
||
" )\n",
|
||
"\n",
|
||
" def get_wmw_stats(\n",
|
||
" first_val: str,\n",
|
||
" second_val: str,\n",
|
||
" interval_type: str, # 时间间隔类型\n",
|
||
" df: pd.DataFrame,\n",
|
||
" alternative: str = \"two-sided\", # 假设类型\n",
|
||
" ):\n",
|
||
" # 检验first_val的interval_type的分布是否小于seconde_val\n",
|
||
" first_df = df[df[\"scope\"] == first_val]\n",
|
||
" second_df = df[df[\"scope\"] == second_val]\n",
|
||
" # 执行WMW检验\n",
|
||
" u_stat, p_value = mannwhitneyu(\n",
|
||
" first_df[interval_type], second_df[interval_type], alternative=alternative\n",
|
||
" )\n",
|
||
" return p_value\n",
|
||
"\n",
|
||
" def get_cliff_delta(\n",
|
||
" first_val: str,\n",
|
||
" second_val: str,\n",
|
||
" interval_type: str, # 时间间隔类型\n",
|
||
" df: pd.DataFrame,\n",
|
||
" ):\n",
|
||
" first_df = df[df[\"scope\"] == first_val]\n",
|
||
" second_df = df[df[\"scope\"] == second_val]\n",
|
||
" # 计算Cliff Delta效应值\n",
|
||
" delta, res = cliffs_delta(\n",
|
||
" first_df[interval_type].dt.total_seconds(),\n",
|
||
" second_df[interval_type].dt.total_seconds(),\n",
|
||
" )\n",
|
||
" return delta, res\n",
|
||
"\n",
|
||
" # 裁切出使用到的列，并排除Epic、Subtask两类链接\n",
|
||
" link_df = link_df[\n",
|
||
" [\n",
|
||
" \"link_type\",\n",
|
||
" \"in_issue_key\",\n",
|
||
" \"out_issue_key\",\n",
|
||
" \"link_created_time\",\n",
|
||
" \"created_time_in\",\n",
|
||
" \"created_time_out\",\n",
|
||
" \"scope\",\n",
|
||
" ]\n",
|
||
" ]\n",
|
||
" link_df = link_df[\n",
|
||
" (link_df[\"link_type\"] != \"Epic\") & (link_df[\"link_type\"] != \"Subtask\")\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # 删除掉link_created_time、created_time_in、created_time_out为空的行\n",
|
||
" link_df = link_df.dropna(\n",
|
||
" subset=[\"link_created_time\", \"created_time_in\", \"created_time_out\"], how=\"any\"\n",
|
||
" )\n",
|
||
" print(\n",
|
||
" f\"✔ {len(link_df)} links after removing links with null link/issue created time\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 创建CTI、LTI两列\n",
|
||
" link_df[\"cti\"] = (link_df[\"created_time_in\"] - link_df[\"created_time_out\"]).abs()\n",
|
||
" max_created_time = link_df[[\"created_time_in\", \"created_time_out\"]].max(axis=1)\n",
|
||
" link_df[\"lti\"] = link_df[\"link_created_time\"] - max_created_time\n",
|
||
" # !注意：有些行的LTI值为负，这是因为获取link_created_time时出错，直接删除对应行\n",
|
||
" link_df = link_df[link_df[\"lti\"] >= pd.Timedelta(0)]\n",
|
||
" print(f\"✔ {len(link_df)} links after removing links with negative LTI\")\n",
|
||
"\n",
|
||
" # 统计CTI、LTI两列并计算分布\n",
|
||
"\n",
|
||
" time_interval_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"scope\", # 链接范围\n",
|
||
" \"interval_type\", # 时间间隔类型\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"median\", # 中值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" for scope in [\"with\"] + list(link_df[\"scope\"].unique()):\n",
|
||
" for interval_type in [\"cti\", \"lti\"]:\n",
|
||
" time_interval_df.loc[len(time_interval_df)] = get_time_stats(\n",
|
||
" scope, interval_type, link_df\n",
|
||
" )\n",
|
||
"\n",
|
||
" for interval_type in [\"cti\", \"lti\"]:\n",
|
||
" first_val, second_val = \"in\", \"cross\"\n",
|
||
" p_value_ne = get_wmw_stats(first_val, second_val, interval_type, link_df)\n",
|
||
" p_value_le = get_wmw_stats(\n",
|
||
" first_val, second_val, interval_type, link_df, \"less\"\n",
|
||
" )\n",
|
||
" p_value_ge = get_wmw_stats(\n",
|
||
" first_val, second_val, interval_type, link_df, \"greater\"\n",
|
||
" )\n",
|
||
" delta, res = get_cliff_delta(first_val, second_val, interval_type, link_df)\n",
|
||
" print(f\"{interval_type.upper()}\")\n",
|
||
" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
|
||
" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
|
||
" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
|
||
" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
|
||
"\n",
|
||
" # 定义特定时间刻度（秒）\n",
|
||
" times_in_seconds = {\n",
|
||
" \"1 minute\": 60,\n",
|
||
" \"1 hour\": 3600,\n",
|
||
" \"1 day\": 86400,\n",
|
||
" \"1 month\": 86400 * 30,\n",
|
||
" \"1 year\": 86400 * 365,\n",
|
||
" }\n",
|
||
" for interval_type in [\"cti\", \"lti\"]:\n",
|
||
" # 转换 timedelta 为秒数\n",
|
||
" offset = 0\n",
|
||
" link_df[interval_type + \"_secs\"] = (\n",
|
||
" link_df[interval_type].dt.total_seconds() + offset\n",
|
||
" )\n",
|
||
"\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" # sns.violinplot(\n",
|
||
" # x=\"scope\", y=(interval_type + \"_secs\"), data=link_df, scale=\"width\"\n",
|
||
" # )\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=link_df,\n",
|
||
" x=\"scope\",\n",
|
||
" y=(interval_type + \"_secs\"),\n",
|
||
" palette=\"pastel\",\n",
|
||
" width=0.5,\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{interval_type.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{interval_type.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" plt.savefig(RQ1_RESULT_DIR / (interval_type + \"_dist_gen.pdf\"), format=\"pdf\")\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return time_interval_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"time_interval_gen = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3.讨论规模"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_comment_scale(\n",
|
||
" eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame, is_gen=False\n",
|
||
"):\n",
|
||
" # 统计有无链接、不同范围链接的评论数量\n",
|
||
"\n",
|
||
" # 排除Epic、Subtask链接的一般类型链接\n",
|
||
" if is_gen:\n",
|
||
" print(\"❕ Without Epic and Subtask Links\")\n",
|
||
" link_df = link_df[\n",
|
||
" (link_df[\"link_type\"] != \"Epic\") & (link_df[\"link_type\"] != \"Subtask\")\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # 取出有链接、无链接、不同范围链接的Issue关键字\n",
|
||
" issues_wlink = set(link_df[\"in_issue_key\"]).union(set(link_df[\"out_issue_key\"]))\n",
|
||
" link_df_in_pro = link_df[link_df[\"scope\"] == \"in\"]\n",
|
||
" issues_wlink_in_pro = set(link_df_in_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_in_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" link_df_cross_pro = link_df[link_df[\"scope\"] == \"cross\"]\n",
|
||
" issues_wlink_cross_pro = set(link_df_cross_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_cross_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" issues_wlink_scopes = {\n",
|
||
" \"in\": issues_wlink_in_pro,\n",
|
||
" \"cross\": issues_wlink_cross_pro,\n",
|
||
" }\n",
|
||
"\n",
|
||
" def get_comment_stats(scope: str):\n",
|
||
" # 所有有链接Issue\n",
|
||
" if scope == \"with\":\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 所有无链接Issue\n",
|
||
" elif scope == \"without\":\n",
|
||
" df = issue_df[~issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 不同链接范围的Issue\n",
|
||
" else:\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink_scopes[scope])]\n",
|
||
"\n",
|
||
" # 计算各个统计指标\n",
|
||
" max_comments = df[\"num_comments\"].max()\n",
|
||
" min_comments = df[\"num_comments\"].min()\n",
|
||
" median_comments = df[\"num_comments\"].median()\n",
|
||
" mean_comments = df[\"num_comments\"].mean()\n",
|
||
" std_comments = df[\"num_comments\"].std()\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" scope,\n",
|
||
" max_comments,\n",
|
||
" min_comments,\n",
|
||
" median_comments,\n",
|
||
" mean_comments,\n",
|
||
" std_comments,\n",
|
||
" )\n",
|
||
"\n",
|
||
" def get_wmw_stats(\n",
|
||
" first_df: pd.DataFrame,\n",
|
||
" second_df: pd.DataFrame,\n",
|
||
" compare_col: str, # 待比较的字段\n",
|
||
" alternative: str = \"two-sided\", # 假设类型\n",
|
||
" ):\n",
|
||
"\n",
|
||
" # 执行WMW检验\n",
|
||
" u_stat, p_value = mannwhitneyu(\n",
|
||
" first_df[compare_col], second_df[compare_col], alternative=alternative\n",
|
||
" )\n",
|
||
" return p_value\n",
|
||
"\n",
|
||
" def get_cliff_delta(\n",
|
||
" first_df: pd.DataFrame,\n",
|
||
" second_df: pd.DataFrame,\n",
|
||
" compare_col: str,\n",
|
||
" ):\n",
|
||
"\n",
|
||
" # 计算Cliff Delta效应值\n",
|
||
" delta, res = cliffs_delta(\n",
|
||
" first_df[compare_col],\n",
|
||
" second_df[compare_col],\n",
|
||
" )\n",
|
||
" return delta, res\n",
|
||
"\n",
|
||
" # 统计评论数量\n",
|
||
" comment_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"scope\", # 链接范围\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"median\", # 中值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" for scope in [\"without\", \"with\"] + link_df[\"scope\"].unique().tolist():\n",
|
||
" comment_df.loc[len(comment_df)] = get_comment_stats(scope)\n",
|
||
"\n",
|
||
" # 执行WMW假设检验\n",
|
||
" # 有无链接的讨论规模\n",
|
||
" for compare_col in [\"num_comments\"]:\n",
|
||
" first_val, second_val = \"without\", \"with\"\n",
|
||
" first_df = issue_df[~issue_df[\"key\"].isin(issues_wlink)].copy()\n",
|
||
" second_df = issue_df[issue_df[\"key\"].isin(issues_wlink)].copy()\n",
|
||
" p_value_ne = get_wmw_stats(first_df, second_df, compare_col)\n",
|
||
" p_value_le = get_wmw_stats(first_df, second_df, compare_col, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_df, second_df, compare_col, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_df, second_df, compare_col)\n",
|
||
"\n",
|
||
" print(f\"{compare_col.upper()}\")\n",
|
||
" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
|
||
" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
|
||
" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
|
||
" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
|
||
"\n",
|
||
" # 将两个DataFrame合并为一个，以便于绘图\n",
|
||
" # 添加“scope”列标识数据来源\n",
|
||
" first_df[\"scope\"] = first_val\n",
|
||
" second_df[\"scope\"] = second_val\n",
|
||
" combined_df = pd.concat([first_df, second_df])\n",
|
||
" offset = 0\n",
|
||
" combined_df[compare_col] = combined_df[compare_col] + offset\n",
|
||
"\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" # sns.violinplot(x=\"scope\", y=compare_col, data=combined_df, scale=\"width\")\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=combined_df, x=\"scope\", y=compare_col, palette=\"pastel\", width=0.5\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{compare_col.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{compare_col.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" save_name = \"_\".join([compare_col, first_val, second_val])\n",
|
||
" save_name += \"_dist_gen.pdf\" if is_gen else \"_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ1_RESULT_DIR / save_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" # 不同范围链接的讨论规模\n",
|
||
" for compare_col in [\"num_comments\"]:\n",
|
||
" first_val, second_val = \"in\", \"cross\"\n",
|
||
" first_df = issue_df[issue_df[\"key\"].isin(issues_wlink_scopes[first_val])].copy()\n",
|
||
" second_df = issue_df[\n",
|
||
" issue_df[\"key\"].isin(issues_wlink_scopes[second_val])\n",
|
||
" ].copy()\n",
|
||
" p_value_ne = get_wmw_stats(first_df, second_df, compare_col)\n",
|
||
" p_value_le = get_wmw_stats(first_df, second_df, compare_col, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_df, second_df, compare_col, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_df, second_df, compare_col)\n",
|
||
"\n",
|
||
" print(f\"{compare_col.upper()}\")\n",
|
||
" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
|
||
" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
|
||
" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
|
||
" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
|
||
"\n",
|
||
" # 绘制对比豆图\n",
|
||
" # 将两个DataFrame合并为一个，以便于绘图\n",
|
||
" # 添加“scope”列标识数据来源\n",
|
||
" first_df[\"scope\"] = first_val\n",
|
||
" second_df[\"scope\"] = second_val\n",
|
||
" combined_df = pd.concat([first_df, second_df])\n",
|
||
" offset = 0\n",
|
||
" combined_df[compare_col] = combined_df[compare_col] + offset\n",
|
||
"\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" # sns.violinplot(x=\"scope\", y=compare_col, data=combined_df, scale=\"width\")\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=combined_df, x=\"scope\", y=compare_col, palette=\"pastel\", width=0.5\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{compare_col.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{compare_col.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" save_name = \"_\".join([compare_col, first_val, second_val])\n",
|
||
" save_name += \"_dist_gen.pdf\" if is_gen else \"_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ1_RESULT_DIR / save_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return comment_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"comment_scale = pd.DataFrame()\n",
|
||
"comment_scale_gen = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.解决比例"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_solved_proportion(\n",
|
||
" eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame, is_gen=False\n",
|
||
"):\n",
|
||
" # 统计有无链接、不同范围链接的Issue解决比例\n",
|
||
"\n",
|
||
" # 排除Epic、Subtask链接的一般类型链接\n",
|
||
" if is_gen:\n",
|
||
" print(\"❕ Without Epic and Subtask Links\")\n",
|
||
" link_df = link_df[\n",
|
||
" (link_df[\"link_type\"] != \"Epic\") & (link_df[\"link_type\"] != \"Subtask\")\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # 取出有链接、无链接、不同范围链接的Issue关键字\n",
|
||
" issues_wlink = set(link_df[\"in_issue_key\"]).union(set(link_df[\"out_issue_key\"]))\n",
|
||
" link_df_in_pro = link_df[link_df[\"scope\"] == \"in\"]\n",
|
||
" issues_wlink_in_pro = set(link_df_in_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_in_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" link_df_cross_pro = link_df[link_df[\"scope\"] == \"cross\"]\n",
|
||
" issues_wlink_cross_pro = set(link_df_cross_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_cross_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" issues_wlink_scopes = {\n",
|
||
" \"in\": issues_wlink_in_pro,\n",
|
||
" \"cross\": issues_wlink_cross_pro,\n",
|
||
" }\n",
|
||
"\n",
|
||
" def get_sloved_stats(scope: str, stat_type: str = \"status\"):\n",
|
||
" # 所有有链接Issue\n",
|
||
" if scope == \"with\":\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 所有无链接Issue\n",
|
||
" elif scope == \"without\":\n",
|
||
" df = issue_df[~issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 不同链接范围的Issue\n",
|
||
" else:\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink_scopes[scope])]\n",
|
||
"\n",
|
||
" # 当前范围关联的Issue总数\n",
|
||
" num_issues = len(df)\n",
|
||
" # 当前范围关联的非Closed Issue总数\n",
|
||
" num_not_closed = (df[stat_type] != \"Closed\").sum()\n",
|
||
" # 当前范围关联的非Closed Issue比例\n",
|
||
" per_not_closed = round(num_not_closed / num_issues * 100, 2)\n",
|
||
" # 当前范围关联的Closed Issue总数\n",
|
||
" num_closed = (df[stat_type] == \"Closed\").sum()\n",
|
||
" # 当前范围关联的Closed Issue比例\n",
|
||
" per_closed = round(num_closed / num_issues * 100, 2)\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" scope,\n",
|
||
" num_issues,\n",
|
||
" num_not_closed,\n",
|
||
" per_not_closed,\n",
|
||
" num_closed,\n",
|
||
" per_closed,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 统计Issue解决比例\n",
|
||
" solved_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"scope\", # 链接范围\n",
|
||
" \"num_issues\", # 该范围关联的Issue总数\n",
|
||
" \"num_not_closed\", # 该范围关联的非Closed Issue总数\n",
|
||
" \"per_not_closed\", # 该范围关联的非Closed Issue比例\n",
|
||
" \"num_closed\", # 该范围关联的Closed Issue总数\n",
|
||
" \"per_closed\", # 该范围关联的Closed Issue比例\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" for scope in [\"without\", \"with\"] + link_df[\"scope\"].unique().tolist():\n",
|
||
" solved_df.loc[len(solved_df)] = get_sloved_stats(scope)\n",
|
||
"\n",
|
||
" # 检验不同链接范围的Issue的解决比例是否有差异\n",
|
||
" scope_pairs = [(\"without\", \"with\"), (\"in\", \"cross\")]\n",
|
||
" for first_val, second_val in scope_pairs:\n",
|
||
" # 构造列联表\n",
|
||
" table = [\n",
|
||
" [\n",
|
||
" solved_df[solved_df[\"scope\"] == first_val][\"num_closed\"].item(),\n",
|
||
" solved_df[solved_df[\"scope\"] == first_val][\"num_not_closed\"].item(),\n",
|
||
" ],\n",
|
||
" [\n",
|
||
" solved_df[solved_df[\"scope\"] == second_val][\"num_closed\"].item(),\n",
|
||
" solved_df[solved_df[\"scope\"] == second_val][\"num_not_closed\"].item(),\n",
|
||
" ],\n",
|
||
" ]\n",
|
||
" # 进行卡方检验\n",
|
||
" chi2, p, dof, expected = chi2_contingency(table)\n",
|
||
"\n",
|
||
" print(\"Solved Proportion:\")\n",
|
||
" print(f\"{first_val} & {second_val}, X^2: {chi2:.3f} p: {p:.3f}\")\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return solved_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"solved_proportion = pd.DataFrame()\n",
|
||
"solved_proportion_gen = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5.解决时长"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_sti(eco_name: str, issue_df: pd.DataFrame, link_df: pd.DataFrame, is_gen=False):\n",
|
||
" # 统计有无链接、不同范围链接的Issue解决时长\n",
|
||
"\n",
|
||
" # 排除Epic、Subtask链接的一般类型链接\n",
|
||
" if is_gen:\n",
|
||
" print(\"❕ Without Epic and Subtask Links\")\n",
|
||
" link_df = link_df[\n",
|
||
" (link_df[\"link_type\"] != \"Epic\") & (link_df[\"link_type\"] != \"Subtask\")\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # 取出有链接、无链接、不同范围链接的Issue关键字\n",
|
||
" issues_wlink = set(link_df[\"in_issue_key\"]).union(set(link_df[\"out_issue_key\"]))\n",
|
||
" link_df_in_pro = link_df[link_df[\"scope\"] == \"in\"]\n",
|
||
" issues_wlink_in_pro = set(link_df_in_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_in_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" link_df_cross_pro = link_df[link_df[\"scope\"] == \"cross\"]\n",
|
||
" issues_wlink_cross_pro = set(link_df_cross_pro[\"in_issue_key\"]).union(\n",
|
||
" set(link_df_cross_pro[\"out_issue_key\"])\n",
|
||
" )\n",
|
||
" issues_wlink_scopes = {\n",
|
||
" \"in\": issues_wlink_in_pro,\n",
|
||
" \"cross\": issues_wlink_cross_pro,\n",
|
||
" }\n",
|
||
"\n",
|
||
" def get_filter(df: pd.DataFrame):\n",
|
||
" # 选出状态为Closed、且STI非空的Issue\n",
|
||
"\n",
|
||
" return (df[\"status\"] == \"Closed\") & (df[\"sti\"].notna())\n",
|
||
"\n",
|
||
" def get_sti_stats(scope: str):\n",
|
||
" # 所有有链接Issue\n",
|
||
" if scope == \"with\":\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 所有无链接Issue\n",
|
||
" elif scope == \"without\":\n",
|
||
" df = issue_df[~issue_df[\"key\"].isin(issues_wlink)]\n",
|
||
" # 不同链接范围的Issue\n",
|
||
" else:\n",
|
||
" df = issue_df[issue_df[\"key\"].isin(issues_wlink_scopes[scope])]\n",
|
||
"\n",
|
||
" # 选出状态为Closed、且STI非空的Issue\n",
|
||
" df = df[get_filter(df)]\n",
|
||
"\n",
|
||
" # 计算各个统计指标\n",
|
||
" max_sti = df[\"sti\"].max().days\n",
|
||
" min_sti = df[\"sti\"].min().days\n",
|
||
" median_sti = df[\"sti\"].median().days\n",
|
||
" mean_sti = df[\"sti\"].mean().days\n",
|
||
" std_sti = df[\"sti\"].std().days\n",
|
||
"\n",
|
||
" return (\n",
|
||
" eco_name,\n",
|
||
" scope,\n",
|
||
" max_sti,\n",
|
||
" min_sti,\n",
|
||
" median_sti,\n",
|
||
" mean_sti,\n",
|
||
" std_sti,\n",
|
||
" )\n",
|
||
"\n",
|
||
" def get_wmw_stats(\n",
|
||
" first_df: pd.DataFrame,\n",
|
||
" second_df: pd.DataFrame,\n",
|
||
" compare_col: str, # 待比较的字段\n",
|
||
" alternative: str = \"two-sided\", # 假设类型\n",
|
||
" ):\n",
|
||
"\n",
|
||
" # 执行WMW检验\n",
|
||
" u_stat, p_value = mannwhitneyu(\n",
|
||
" first_df[compare_col], second_df[compare_col], alternative=alternative\n",
|
||
" )\n",
|
||
" return p_value\n",
|
||
"\n",
|
||
" def get_cliff_delta(\n",
|
||
" first_df: pd.DataFrame,\n",
|
||
" second_df: pd.DataFrame,\n",
|
||
" compare_col: str,\n",
|
||
" ):\n",
|
||
"\n",
|
||
" # 计算Cliff Delta效应值\n",
|
||
" delta, res = cliffs_delta(\n",
|
||
" first_df[compare_col].dt.total_seconds(),\n",
|
||
" second_df[compare_col].dt.total_seconds(),\n",
|
||
" )\n",
|
||
" return delta, res\n",
|
||
"\n",
|
||
" # 创建STI（解决时长）列\n",
|
||
" issue_df[\"sti\"] = issue_df[\"closed_time\"] - issue_df[\"created_time\"]\n",
|
||
"\n",
|
||
" # 统计解决时长\n",
|
||
" sti_df = pd.DataFrame(\n",
|
||
" columns=[\n",
|
||
" \"ecosystem\", # 生态名\n",
|
||
" \"scope\", # 链接范围\n",
|
||
" \"max\", # 最大值\n",
|
||
" \"min\", # 最小值\n",
|
||
" \"median\", # 中值\n",
|
||
" \"mean\", # 平均值\n",
|
||
" \"std\", # 标准差\n",
|
||
" ],\n",
|
||
" )\n",
|
||
"\n",
|
||
" for scope in [\"without\", \"with\"] + link_df[\"scope\"].unique().tolist():\n",
|
||
" sti_df.loc[len(sti_df)] = get_sti_stats(scope)\n",
|
||
"\n",
|
||
" # 定义特定时间刻度（秒）\n",
|
||
" times_in_seconds = {\n",
|
||
" \"1 minute\": 60,\n",
|
||
" \"1 hour\": 3600,\n",
|
||
" \"1 day\": 86400,\n",
|
||
" \"1 month\": 86400 * 30,\n",
|
||
" \"1 year\": 86400 * 365,\n",
|
||
" }\n",
|
||
"\n",
|
||
" # 把timedelta转换为秒数，方便绘图\n",
|
||
" issue_df[\"sti_secs\"] = issue_df[\"sti\"].dt.total_seconds()\n",
|
||
"\n",
|
||
" # 有无链接的解决时长\n",
|
||
" for compare_col in [\"sti\"]:\n",
|
||
" # 执行WMW假设检验\n",
|
||
" first_val, second_val = \"without\", \"with\"\n",
|
||
" first_df = issue_df[~issue_df[\"key\"].isin(issues_wlink)].copy()\n",
|
||
" second_df = issue_df[issue_df[\"key\"].isin(issues_wlink)].copy()\n",
|
||
"\n",
|
||
" first_df = first_df[get_filter(first_df)]\n",
|
||
" second_df = second_df[get_filter(second_df)]\n",
|
||
"\n",
|
||
" p_value_ne = get_wmw_stats(first_df, second_df, compare_col)\n",
|
||
" p_value_le = get_wmw_stats(first_df, second_df, compare_col, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_df, second_df, compare_col, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_df, second_df, compare_col)\n",
|
||
"\n",
|
||
" print(f\"{compare_col.upper()}\")\n",
|
||
" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
|
||
" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
|
||
" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
|
||
" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
|
||
"\n",
|
||
" # 绘制对比豆图\n",
|
||
" # 将两个DataFrame合并为一个，以便于绘图\n",
|
||
" # 添加“scope”列标识数据来源\n",
|
||
" first_df[\"scope\"] = first_val\n",
|
||
" second_df[\"scope\"] = second_val\n",
|
||
" combined_df = pd.concat([first_df, second_df])\n",
|
||
" offset = 0\n",
|
||
" combined_df[\"sti_secs\"] = combined_df[\"sti_secs\"] + offset\n",
|
||
"\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" # sns.violinplot(x=\"scope\", y=\"sti_secs\", data=combined_df, scale=\"width\")\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=combined_df, x=\"scope\", y=\"sti_secs\", palette=\"pastel\", width=0.5\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{compare_col.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{compare_col.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" save_name = \"_\".join([compare_col, first_val, second_val])\n",
|
||
" save_name += \"_dist_gen.pdf\" if is_gen else \"_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ1_RESULT_DIR / save_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" # 不同范围链接的解决时长\n",
|
||
" for compare_col in [\"sti\"]:\n",
|
||
" first_val, second_val = \"in\", \"cross\"\n",
|
||
" first_df = issue_df[issue_df[\"key\"].isin(issues_wlink_scopes[first_val])].copy()\n",
|
||
" second_df = issue_df[\n",
|
||
" issue_df[\"key\"].isin(issues_wlink_scopes[second_val])\n",
|
||
" ].copy()\n",
|
||
"\n",
|
||
" first_df = first_df[get_filter(first_df)]\n",
|
||
" second_df = second_df[get_filter(second_df)]\n",
|
||
"\n",
|
||
" p_value_ne = get_wmw_stats(first_df, second_df, compare_col)\n",
|
||
" p_value_le = get_wmw_stats(first_df, second_df, compare_col, \"less\")\n",
|
||
" p_value_ge = get_wmw_stats(first_df, second_df, compare_col, \"greater\")\n",
|
||
" delta, res = get_cliff_delta(first_df, second_df, compare_col)\n",
|
||
"\n",
|
||
" print(f\"{compare_col.upper()}\")\n",
|
||
" print(f\"{first_val} != {second_val}, p_value: {p_value_ne:.3f}\")\n",
|
||
" print(f\"{first_val} < {second_val}, p_value: {p_value_le:.3f}\")\n",
|
||
" print(f\"{first_val} > {second_val}, p_value: {p_value_ge:.3f}\")\n",
|
||
" print(f\"{first_val} & {second_val}, delta: {delta:.3f}, {res}\")\n",
|
||
"\n",
|
||
" # 绘制对比豆图\n",
|
||
" # 将两个DataFrame合并为一个，以便于绘图\n",
|
||
" # 添加“scope”列标识数据来源\n",
|
||
" first_df[\"scope\"] = first_val\n",
|
||
" second_df[\"scope\"] = second_val\n",
|
||
" combined_df = pd.concat([first_df, second_df])\n",
|
||
" offset = 0\n",
|
||
" combined_df[\"sti_secs\"] = combined_df[\"sti_secs\"] + offset\n",
|
||
"\n",
|
||
" sns.set_theme(style=\"whitegrid\")\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" # sns.violinplot(x=\"scope\", y=\"sti_secs\", data=combined_df, scale=\"width\")\n",
|
||
"\n",
|
||
" # 绘制箱型图\n",
|
||
" sns.boxplot(\n",
|
||
" data=combined_df, x=\"scope\", y=\"sti_secs\", palette=\"pastel\", width=0.5\n",
|
||
" )\n",
|
||
" plt.yscale(\"log\") # 设置y轴为对数刻度\n",
|
||
"\n",
|
||
" # 添加标题和轴标签\n",
|
||
" plt.title(\n",
|
||
" f\"{compare_col.upper()} distribution of different Link scope\", fontsize=20\n",
|
||
" )\n",
|
||
" plt.xlabel(\"Link scope\", fontsize=18)\n",
|
||
" plt.ylabel(f\"{compare_col.upper()} (log)\", fontsize=18)\n",
|
||
" # 刻度值设置为大号\n",
|
||
" plt.tick_params(axis=\"both\", labelsize=\"large\")\n",
|
||
"\n",
|
||
" # 添加特定时间点的标注\n",
|
||
" plt.yticks(list(times_in_seconds.values()), list(times_in_seconds.keys()))\n",
|
||
" plt.tight_layout() # 自动调整子图参数, 使之填充整个图像区域\n",
|
||
" # 保存为PDF\n",
|
||
" save_name = \"_\".join([compare_col, first_val, second_val])\n",
|
||
" save_name += \"_dist_gen.pdf\" if is_gen else \"_dist.pdf\"\n",
|
||
" plt.savefig(\n",
|
||
" RQ1_RESULT_DIR / save_name,\n",
|
||
" format=\"pdf\",\n",
|
||
" )\n",
|
||
" # plt.show()\n",
|
||
"\n",
|
||
" print(\"-\" * 30)\n",
|
||
"\n",
|
||
" return sti_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"sti = pd.DataFrame()\n",
|
||
"sti_gen = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✔ 222681 links after removing links with null link/issue created time\n",
|
||
"✔ 222483 links after removing links with negative LTI\n",
|
||
"CTI\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.110, negligible\n",
|
||
"LTI\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.426, medium\n",
|
||
"------------------------------\n",
|
||
"❕ Without Epic and Subtask Links\n",
|
||
"✔ 114350 links after removing links with null link/issue created time\n",
|
||
"✔ 114278 links after removing links with negative LTI\n",
|
||
"CTI\n",
|
||
"in != cross, p_value: 0.809\n",
|
||
"in < cross, p_value: 0.404\n",
|
||
"in > cross, p_value: 0.596\n",
|
||
"in & cross, delta: -0.001, negligible\n",
|
||
"LTI\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.097, negligible\n",
|
||
"------------------------------\n",
|
||
"NUM_COMMENTS\n",
|
||
"without != with, p_value: 0.000\n",
|
||
"without < with, p_value: 1.000\n",
|
||
"without > with, p_value: 0.000\n",
|
||
"without & with, delta: 0.033, negligible\n",
|
||
"NUM_COMMENTS\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.147, negligible\n",
|
||
"------------------------------\n",
|
||
"❕ Without Epic and Subtask Links\n",
|
||
"NUM_COMMENTS\n",
|
||
"without != with, p_value: 0.000\n",
|
||
"without < with, p_value: 0.000\n",
|
||
"without > with, p_value: 1.000\n",
|
||
"without & with, delta: -0.168, small\n",
|
||
"NUM_COMMENTS\n",
|
||
"in != cross, p_value: 0.416\n",
|
||
"in < cross, p_value: 0.208\n",
|
||
"in > cross, p_value: 0.792\n",
|
||
"in & cross, delta: -0.002, negligible\n",
|
||
"------------------------------\n",
|
||
"Solved Proportion:\n",
|
||
"without & with, X^2: 18.381 p: 0.000\n",
|
||
"Solved Proportion:\n",
|
||
"in & cross, X^2: 1346.772 p: 0.000\n",
|
||
"------------------------------\n",
|
||
"❕ Without Epic and Subtask Links\n",
|
||
"Solved Proportion:\n",
|
||
"without & with, X^2: 14.589 p: 0.000\n",
|
||
"Solved Proportion:\n",
|
||
"in & cross, X^2: 1454.019 p: 0.000\n",
|
||
"------------------------------\n",
|
||
"STI\n",
|
||
"without != with, p_value: 0.000\n",
|
||
"without < with, p_value: 0.000\n",
|
||
"without > with, p_value: 1.000\n",
|
||
"without & with, delta: -0.021, negligible\n",
|
||
"STI\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.223, small\n",
|
||
"------------------------------\n",
|
||
"❕ Without Epic and Subtask Links\n",
|
||
"STI\n",
|
||
"without != with, p_value: 0.000\n",
|
||
"without < with, p_value: 0.000\n",
|
||
"without > with, p_value: 1.000\n",
|
||
"without & with, delta: -0.103, negligible\n",
|
||
"STI\n",
|
||
"in != cross, p_value: 0.000\n",
|
||
"in < cross, p_value: 0.000\n",
|
||
"in > cross, p_value: 1.000\n",
|
||
"in & cross, delta: -0.185, small\n",
|
||
"------------------------------\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
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]
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},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
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"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"for eco_name in ECO_NAMES:\n",
|
||
" # 加载数据\n",
|
||
" issue_df, link_df = load_df(eco_name)\n",
|
||
"\n",
|
||
" # 添加链接范围信息\n",
|
||
" link_df = add_link_scope(link_df)\n",
|
||
"\n",
|
||
" # 基本信息\n",
|
||
" overview.loc[len(overview)] = get_overview(eco_name, issue_df, link_df)\n",
|
||
"\n",
|
||
" # CTI、LTI信息\n",
|
||
" time_interval = pd.concat(\n",
|
||
" [time_interval, get_time_interval(eco_name, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 一般类型（排除Epic、Subtask两类链接）CTI、LTI信息\n",
|
||
" time_interval_gen = pd.concat(\n",
|
||
" [time_interval_gen, get_time_interval_gen(eco_name, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 评论信息\n",
|
||
" comment_scale = pd.concat(\n",
|
||
" [comment_scale, get_comment_scale(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
" # 一般类型评论信息\n",
|
||
" comment_scale_gen = pd.concat(\n",
|
||
" [comment_scale_gen, get_comment_scale(eco_name, issue_df, link_df, True)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 解决比例\n",
|
||
" solved_proportion = pd.concat(\n",
|
||
" [solved_proportion, get_solved_proportion(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" solved_proportion_gen = pd.concat(\n",
|
||
" [\n",
|
||
" solved_proportion_gen,\n",
|
||
" get_solved_proportion(eco_name, issue_df, link_df, True),\n",
|
||
" ],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 解决时长\n",
|
||
" sti = pd.concat(\n",
|
||
" [sti, get_sti(eco_name, issue_df, link_df)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
" sti_gen = pd.concat(\n",
|
||
" [sti_gen, get_sti(eco_name, issue_df, link_df, True)],\n",
|
||
" ignore_index=True,\n",
|
||
" sort=False,\n",
|
||
" )\n",
|
||
"\n",
|
||
"\n",
|
||
"overview.to_csv(RQ1_RESULT_DIR / \"eco_overview.csv\", sep=\",\", index=False)\n",
|
||
"time_interval.to_csv(RQ1_RESULT_DIR / \"time_interval.csv\", sep=\",\", index=False)\n",
|
||
"time_interval_gen.to_csv(RQ1_RESULT_DIR / \"time_interval_gen.csv\", sep=\",\", index=False)\n",
|
||
"comment_scale.to_csv(RQ1_RESULT_DIR / \"comment_scale.csv\", sep=\",\", index=False)\n",
|
||
"comment_scale_gen.to_csv(RQ1_RESULT_DIR / \"comment_scale_gen.csv\", sep=\",\", index=False)\n",
|
||
"solved_proportion.to_csv(RQ1_RESULT_DIR / \"sloved_proportion.csv\", sep=\",\", index=False)\n",
|
||
"solved_proportion_gen.to_csv(\n",
|
||
" RQ1_RESULT_DIR / \"sloved_proportion_gen.csv\", sep=\",\", index=False\n",
|
||
")\n",
|
||
"sti.to_csv(RQ1_RESULT_DIR / \"sti.csv\", sep=\",\", index=False)\n",
|
||
"sti_gen.to_csv(RQ1_RESULT_DIR / \"sti_gen.csv\", sep=\",\", index=False)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "grad_pro_env",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|