Access Control Effector - Response-Time Behavior.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Access Control Effector - Response-Time Behavior\n",
    "\n",
    "Before you can use this notebook, data must be prepared:\n",
    "* Run ```fix-all-log-files.sh``` on the logs to fix potential newline issues with certain Kieker versions.\n",
    "* Compute the response-times with ```run-all-response-time-ac-probe.sh```\n",
    "* Check whether the ```data_prefix``` is set correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Reading data/probe-experiment-node2/exp-1/1/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/2/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/3/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/4/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/5/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/6/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/7/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/8/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/9/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/10/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/11/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/12/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/13/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/14/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/15/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/16/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/17/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/18/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/19/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/20/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/30/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/40/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/50/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/60/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/70/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/80/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/90/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/95/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/100/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/110/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/120/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/130/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/140/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/150/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/200/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/300/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/400/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/500/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/550/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/560/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/570/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/580/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/590/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/600/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/700/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/710/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/720/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/730/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/740/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/750/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/800/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/900/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/950/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/960/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/970/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/980/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/990/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1010/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1020/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1030/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1040/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/1050/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/2000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/3000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/4000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/5000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/6000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/7000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/8000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/9000/response-time/response-time.csv ...\"\n",
      "[1] \"Reading data/probe-experiment-node2/exp-1/10000/response-time/response-time.csv ...\"\n"
     ]
    }
   ],
   "source": [
    "data_prefix <- \"data/probe-experiment-node2/exp-1\"\n",
    "\n",
    "operation_name <- 'public abstract interface void org.openqa.selenium.WebElement.click()'\n",
    "task_name <- 'org.iobserve.selenium.behavior.tasks.jpetstore.account.ChangeAccountInformationTask'\n",
    "#'org.iobserve.selenium.behavior.tasks.jpetstore.common.LoginJPetStoreTask'\n",
    "\n",
    "size=c()\n",
    "mean_rst=c()\n",
    "median_rst=c()\n",
    "q1_rst=c()\n",
    "q3_rst=c()\n",
    "\n",
    "collected_index=c()\n",
    "collected_values=c()\n",
    "\n",
    "# read data\n",
    "for (loop in c(1,2,3,4,5,6,7,8,9,\n",
    "                     10,11,12,13,14,15,16,17,18,19,\n",
    "                     20,30,40,50,60,70,80,90,95,\n",
    "                     100,110,120,130,140,150,\n",
    "                     200,300,400,\n",
    "                     500,550,560,570,580,590,\n",
    "                     600,\n",
    "                     700,710,720,730,740,750,\n",
    "                     800,\n",
    "                     900,950,960,970,980,990,\n",
    "                     1000,1010,1020,1030,1040,1050,\n",
    "                     2000,3000,4000,5000,6000,7000,8000,9000,\n",
    "                     10000)) {\n",
    "    responseTimePath <- sprintf(\"%s/%s/response-time/response-time.csv\",data_prefix, loop)\n",
    "\n",
    "    if (file.exists(responseTimePath)) {\n",
    "        if (file.size(responseTimePath) > 0) {\n",
    "            print(sprintf(\"Reading %s ...\", responseTimePath))\n",
    "            input <- read.csv(responseTimePath, header=TRUE)\n",
    "\n",
    "            clickResponseTime <- input[ which(input$operation==operation_name & input$task==task_name & input$event.type=='AfterOperationEvent'), ]\n",
    "            \n",
    "            size <- c(size,loop)\n",
    "            mean_rst <- c(mean_rst, mean(clickResponseTime[,3]))\n",
    "            median_rst <- c(median_rst, median(clickResponseTime[,3]))\n",
    "            q1_rst <- c(q1_rst, quantile(clickResponseTime[,3], 0.25))\n",
    "            q3_rst <- c(q3_rst, quantile(clickResponseTime[,3], 0.75))\n",
    "            \n",
    "            for (i in 1:length(clickResponseTime[,1])) {\n",
    "                collected_index <- c(collected_index, loop)\n",
    "                collected_values <- c(collected_values, clickResponseTime[i,3])\n",
    "            }\n",
    "        } else {\n",
    "            print(sprintf(\"File %s is empty.\", responseTimePath))\n",
    "        }\n",
    "    } else {\n",
    "        print(sprintf(\"File %s is missing\", responseTimePath))\n",
    "    }  \n",
    "}\n",
    "\n",
    "data <- data.frame(\"index\"=size, \"mean\"=mean_rst, \"q1\"=q1_rst, \"q3\"=q3_rst, \"median\"=median_rst, stringsAsFactors=FALSE) \n",
    "collected <- data.frame(\"index\"=collected_index, \"value\"=collected_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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DHGnPf2I/wCtAPxZ8rj8TgyhYNQpMEu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\nJVbQwopUkyBNM7sQAIDigmBnb5kJ53LXF3bAcNnVMOEreT0YCc8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fDz74YG5dfn19fT7XzOexLXqjiOnK4cOHN79x2LBhRCRJUvPNGYcO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fsmlDWhZDvpeYcfOYyQDQ8bJ7Bwkioinq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\n2aEtfS1hozNgoHntTay+tsxk0ShFoyxq1oc206BofTJjlsWs+ltbeUQuyaS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gAAoBNDCPAFzohIDGDwBAAAQKeGplhf4ERE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\n2cTDPESeKL5SI4uXMlqKooVGow/IOQNDS/84VD1S7REzh5w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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(magrittr)\n",
    "library(ggpubr)\n",
    "library(repr)\n",
    "library(reshape2)\n",
    "library(nlme)\n",
    "\n",
    "\n",
    "allColumns <- colnames(data)\n",
    "\n",
    "measureColumns <- allColumns[c(2,3, 4, 5)]\n",
    "\n",
    "ggData <- melt(data, id.vars = \"index\", measure.vars = measureColumns)\n",
    "\n",
    "ggplot(ggData, aes(x=index, y=value, linetype = variable, color = variable)) + \n",
    "    geom_line() +\n",
    "    geom_point() + \n",
    "    xlab(\"Model Size (instances)\") + \n",
    "    ylab(\"Response Time (ns)\")\n",
    "# show click, clear\n",
    "#\n",
    "#ggplot(data = responseTime, aes(x=operation, y=response.time)) + \n",
    "#    geom_boxplot(aes(fill=operation)) + \n",
    "#    scale_fill_discrete(name = \"Functions\", labels = c(\"clear\", \"click\", \"sendKeys\")) +\n",
    "#    scale_x_discrete(breaks=c(\"public abstract interface void org.openqa.selenium.WebElement.clear()\",\n",
    "#                              \"public abstract interface void org.openqa.selenium.WebElement.click()\",\n",
    "#                              \"public abstract transient interface void org.openqa.selenium.WebElement.sendKeys(java.lang.CharSequence[])\"),\n",
    "#        labels=c(\"clear\", \"click\", \"sendKeys\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "easureColumns <- allColumns[c(2,3, 4, 5)]\n",
    "\n",
    "ggData <- melt(data, id.vars = \"index\", measure.vars = measureColumns)\n",
    "\n",
    "g <- ggplot(ggData, aes(x=index, y=value, linetype = variable, color = variable)) + \n",
    "    geom_line() +\n",
    "    geom_point() + \n",
    "    xlab(\"Model Size (instances)\") + \n",
    "    ylab(\"Response Time (ns)\") +\n",
    "    theme(\n",
    "        axis.title = element_text(size = 8),\n",
    "        axis.text = element_text(size = 6),\n",
    "        legend.text = element_text(size = 6),\n",
    "        legend.title = element_text(size = 8)\n",
    "    )\n",
    "\n",
    "ggsave(g, width=123, height=50, filename=\"access-control-probe-performance.pdf\", units=\"mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.machinelearningplus.com/machine-learning/complete-introduction-linear-regression-r/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = index ~ median, data = data)\n",
      "\n",
      "Coefficients:\n",
      "(Intercept)       median  \n",
      "  9.887e+02    1.002e-04  \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "-0.00833137233398907"
      ],
      "text/latex": [
       "-0.00833137233398907"
      ],
      "text/markdown": [
       "-0.00833137233398907"
      ],
      "text/plain": [
       "[1] -0.008331372"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linearMod <- lm(index ~ median, data=data)\n",
    "print(linearMod)\n",
    "\n",
    "cor(collected$index, collected$value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vx9sXvr1q3b9gjYfccxjc75SVTLK0VF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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(collected, aes(x=collected$index, y=collected$value)) + \n",
    "    ggtitle(\"All response time measurement related to the model sizes\") +\n",
    "    geom_line() +\n",
    "    geom_point() + \n",
    "    xlab(\"Model Size (instances)\") + \n",
    "    ylab(\"Execution Time (ns)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}