2b:null 2c:["$","title",null,{"children":"Monte Carlo Simulation for Agile Software Development, Budget Prognosis and Replacement for Extrapolation | Daniel Frey | Observable"}] 31:Tc05,md` ## Summary Debug value = ${new Date(finishedAtWith(startDate, sprintLength, 0.01))} Debug value = ${new Date(finishedAtWith(startDate, sprintLength, 0.01)).toISOString()} |Parameters|| |---|---:| |Number of items implemented in the past|${historicalData[0].reduce( (a, b) => +a + +b, 0 )}| |Number of completed iterations in the past|${historicalData[0].length}| |Number of remaining items to be implemented in the future|${remainingStories}| |Start date of the next iteration|${startDate}| |Iteration length [ex. weeks]|${sprintLength}| |Number of simulations used to calculate probabilities|${numberOfSimultations}| ||| |Results|Future Iterations|ETA| |---|:---:|---:| |With 99% Probability|${Math.ceil(totalIterations(cumulated, 0.99) + 1)} (${ Math.round((totalIterations(cumulated, 0.99) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.99)) .toISOString() .split("T")[0] }| |With 95% Probability|${Math.ceil(totalIterations(cumulated, 0.95) + 1)} (${ Math.round((totalIterations(cumulated, 0.95) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.95)) .toISOString() .split("T")[0] }| |With 90% Probability|${Math.ceil(totalIterations(cumulated, 0.9) + 1)} (${ Math.round((totalIterations(cumulated, 0.9) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.9)) .toISOString() .split("T")[0] }| |With 85% Probability|${Math.ceil(start85 + 1)} (${ Math.round((start85 + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.85)) .toISOString() .split("T")[0] }| |**With 50% Probability**|**${Math.ceil(start50 + 1)} (${ Math.round((start50 + 1) * 10) / 10 })**|**${ new Date(finishedAtWith(startDate, sprintLength, 0.5)) .toISOString() .split("T")[0] }**| |With 15% Probability|${Math.ceil(totalIterations(cumulated, 0.15) + 1)} (${ Math.round((totalIterations(cumulated, 0.15) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.15)) .toISOString() .split("T")[0] }| |With 10% Probability|${Math.ceil(totalIterations(cumulated, 0.1) + 1)} (${ Math.round((totalIterations(cumulated, 0.1) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.1)) .toISOString() .split("T")[0] }| |With 5% Probability|${Math.ceil(totalIterations(cumulated, 0.05) + 1)} (${ Math.round((totalIterations(cumulated, 0.05) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.05)) .toISOString() .split("T")[0] }| |With 1% Probability|${Math.ceil(totalIterations(cumulated, 0.01) + 1)} (${ Math.round((totalIterations(cumulated, 0.01) + 1) * 10) / 10 })|${ new Date(finishedAtWith(startDate, sprintLength, 0.01)) .toISOString() .split("T")[0] }| |In Words|| |---|---:| The **${remainingStories} remaining items** will be completed with a **probability of 70%** between **${Math.floor( start15 + 1 )} and ${Math.ceil(start85 + 1)} iterations** and between **${Math.floor( start05 + 1 )} and ${Math.ceil(start95 + 1)} with 90%**. `32:T65f,c3({ data: { columns: [R.insert(0, "data", cumulated)], type: 'area', colors: { data: 'orange' } }, size: { height: 300 }, axis: { x: { min: -0.3, max: cumulated.length - 0.4, tick: { format: function (x) { return (x+1) + ((x==0) ? " Iteration" : " Iterations"); } } }, y: { label: { text: 'Probability (Cumulated)', position: 'inner-bottom' } }, }, grid: { x: { lines: [ {value: start99, text: (Math.round((start99+1)*10)/10) + ' Sprints', position: 'end'}, {value: start95, text: (Math.round((start95+1)*10)/10) + ' Sprints', position: 'end'}, {value: start85, text: (Math.round((start85+1)*10)/10) + ' Sprints', position: 'end'}, {value: start50, text: (Math.round((start50+1)*10)/10) + ' Sprints', position: 'end'}, {value: start15, text: (Math.round((start15+1)*10)/10) + ' Sprints', position: 'end'}, {value: start05, text: (Math.round((start05+1)*10)/10) + ' Sprints', position: 'end'}, {value: start01, text: (Math.round((start01+1)*10)/10) + ' Sprints', position: 'end'}, ], }, y: { lines: [ {value: 0.85, text: '85% Probabiilty', position: 'end'}, {value: 0.50, text: '50% Probabiilty', position: 'end'}, {value: 0.15, text: '15% Probabiilty', position: 'end'}, ], }, }, regions: [ {start: start85, end: cumulated.length-1, class: 'green'}, {start: start15, end: start85, class: 'orange'}, {start: 0, end: start15, class: 'red'}, ], legend: { show: false } })33:[] 2f:{"id":"321c7d4a70d8fe98","slug":"monte-carlo-simulation-i","title":"Monte Carlo Simulation for Agile Software Development, Budget Prognosis and Replacement for Extrapolation","thumbnail":"022a17762b4c2b96df11a753fa4c0a29eabc44325262ff7385c1d1f56ceb22a1","access_level":"public","create_time":"$D2020-03-04T19:57:05.222Z","update_time":"$D2023-02-28T14:05:17.169Z","view_time":null,"first_public_time":"$D2020-03-06T13:01:41.098Z","fork":false,"stars":6,"starred":false,"owner":{"type":"team","id":"55a7231b9489850a","login":"danielfrey","name":"Daniel Frey","avatar_url":"https://avatars-next.observableusercontent.com/7443b9a35f0a55362837f723bddfde22eef54fb4ac636378e4c94721eabe8e24","tier":"starter_2024"},"version":1349,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"08d5e1747b47cfdc","login":"danielfrey","name":"Daniel Frey","avatar_url":"https://avatars-next.observableusercontent.com/7443b9a35f0a55362837f723bddfde22eef54fb4ac636378e4c94721eabe8e24","tier":null}],"members":[{"type":"individual","id":"08d5e1747b47cfdc","login":"danielfrey","name":"Daniel Frey","avatar_url":"https://avatars-next.observableusercontent.com/7443b9a35f0a55362837f723bddfde22eef54fb4ac636378e4c94721eabe8e24","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":6,"collection":null,"type":"notebook","body":{"title":"Monte Carlo Simulation for Agile Software Development, Budget Prognosis and Replacement for Extrapolation","cells":[{"id":0,"value":"md `# Monte Carlo Simulation for Agile Software Development, Budget Prognosis and Replacement for Extrapolation\n## Introduction\n\nThis notebook calculates the probability distribution of a desired number of items (ie. number of stories, epics, features, capabilities, motives, or sum of story points, complexities, days, weeks, months) to be processed in the future, based on already processed items in the past. Note: Make sure the historical and future data both are in the same context (same team, topic, mvp, project, budget, aso).\n\nThe question we can answer with this notebook is:\n- What is the probability that we will complete a given number of items by a given date?\n- What is the soones and latest date to complete a given number of items?\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":387,"value":"md`## Input Data\n\nWe need a comma-separated list of historical items that have been completed in the past.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":77,"value":"viewof historicalDataText = {\n const textarea = html`

\n13645,13095,14342,5045,8230,1750,11560,7310,12740,13790,17305,11755,13135,16715\n`;\n  // 8,11,15,15,13,10,19,6\n  textarea.style.display = \"block\";\n  textarea.style.boxSizing = \"border-box\";\n  textarea.style.width = \"calc(100% + 28px)\";\n  textarea.style.font = \"var(--mono_fonts)\";\n  textarea.style.minHeight = \"30px\";\n  textarea.style.border = \"none\";\n  textarea.style.padding = \"4px 14px\";\n  textarea.style.margin = \"0 -14px\";\n  textarea.style.background = \"rgb(247,247,249)\";\n  textarea.style.tabSize = 2;\n  textarea.oninput = () => textarea.style.height = `${textarea.value.match(/^/gm).length * 21 + 8}px`;\n  textarea.oninput();\n  return textarea;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":94,"value":"viewof remainingStories = number({value: 230975, submit: true, title: \"<span class='paragraph'>The number of items which you want to complete in the future. Make sure that this number is larger than any number given in the list of numbers above.</span>\"}) // 39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":640,"value":"viewof startDate = date({ value: (new Date()).toISOString().split('T')[0], submit: true, title: 'Start date of the next Iteration' })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":661,"value":"viewof sprintLength = number({value: \"1\", submit: true, title: \"Iteration length (ex. weeks)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1330,"value":"debugvalue = finishedAtWith(startDate, sprintLength, 0.5)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":901,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":618,"value":"md `## Charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":304,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":892,"value":"c3({\n  data: {\n    columns: dataWithHeaders,\n    type: 'line',\n    color: function (color, d) { return (d.id == 'data' ? \"#ffa500DD\" : \"#ffa50011\") }, // \"#79d20411\"\n    types: {\n      data: 'bar'\n    },\n    axes: {\n      data: 'y2'\n    },\n  },\n  point: {\n    show: false\n  },\n  size: {\n    height: 300\n  },\n  axis: {\n    x: {\n      tick: {\n        format: function (x) { return (x+1) + ((x==0) ? \" Iteration\" : \" Iterations\"); }\n      },\n    },\n    y: {\n      max: 2*remainingStories,\n      label: {\n        text: 'Random Item Burnups',\n        position: 'inner-bottom'\n      }\n    },\n    y2: {\n      show: false,\n      //min: -Math.max(...result),\n      label: {\n        text: 'Probabilities'\n      },\n    },\n  },\n  bar: {\n    width: {\n      ratio: 1.1\n    }\n  },\n  grid: {\n    y: {\n      lines: [\n        {value: remainingStories, text: remainingStories + ' Remaining Items', position: 'end'},\n      ],\n    },\n  },\n  legend: {\n    show: false\n  }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":391,"value":"md`## Parameters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":598,"value":"md `The **number of repetitions** you want to run. Usually a number 500 reaches a good significance.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"viewof numberOfSimultations = slider({value: 500, min: 50, max: 1000, step: 50, submit: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":511,"value":"md `***\n## Appendix\n\nFeel free to ignore the stuff in this chapter as it is only the setup for the above calculations.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":868,"value":"md `### Intermediate Results`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1290,"value":"start99 = totalIterations(cumulated, 0.99)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1275,"value":"start95 = totalIterations(cumulated, 0.95)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":325,"value":"start85 = totalIterations(cumulated, 0.85)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":581,"value":"start50 = totalIterations(cumulated, 0.5)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1271,"value":"start15 = totalIterations(cumulated, 0.15)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1276,"value":"start05 = totalIterations(cumulated, 0.05)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1285,"value":"start01 = totalIterations(cumulated, 0.01)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":110,"value":"result = allData[0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":297,"value":"cumulated = cumulate(result)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":775,"value":"burnups = allData[1]; //.map((arr, i) => arr.unshift(\"data\"+i))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":872,"value":"md `### Formulas and Calculations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":19,"value":"function simulate(size, distribution=[], burnups=[]) {\n  while (burnups.length < size) {\n    burnups.push(resample());\n    var countOfSprints = burnups[burnups.length-1].length-1;\n    distribution[countOfSprints] = (distribution[countOfSprints] || 0) + 1;\n  }\n  return [Array.from(distribution, val => (val || 0) / numberOfSimultations), burnups];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":27,"value":"function resample(burnup=[], dc=0) {\n  var lastBurnup = burnup[burnup.length-1] || 0;\n  if (lastBurnup < remainingStories) {\n    burnup.push(lastBurnup + parseInt(historicalData[0][Math.floor(Math.random()*historicalData[0].length)], 10));\n    return resample(burnup, ++dc);\n  }\n  return burnup;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":287,"value":"// Calculates the cumulated values of the given result up to a precision given by the number of simulations.\nfunction cumulate(result) {\n  var cum = [];\n  result.reduce((sum,val,i) => cum[i] = Math.round((sum+val)*numberOfSimultations)/numberOfSimultations, 0);\n  return cum;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":310,"value":"// Returns the number of periods (with fractions) needed to reach the given probability with the given cumulated distribution\nfunction totalIterations(cum, perc) {\n  console.log(cum);\n  console.log(perc);\n  // Position of first value equal or higher than the given probability\n  var i = cum.indexOf(Math.min(...cum.filter((val) => val >= perc)));\n  return i - (cum[i] - perc) / (cum[i] - cum[i - 1]);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":682,"value":"function finishedAtWith(start = Date.now, sprintLen = 2, prob = 0.85) {\n  //console.log(cumulated);\n  //console.log(prob);\n  //console.log(Math.ceil(totalIterations(cumulated, prob) + 1));\n  return (\n    new Date(start).getTime() +\n    1000 *\n      60 *\n      60 *\n      24 *\n      7 *\n      sprintLen *\n      Math.ceil(totalIterations(cumulated, prob) + 1)\n  );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":785,"value":"// Takes a subsample of 500 burnups in order to run smoothly\nfunction getDataWithHeader(r=[], b=[]) {\n  var result = [];\n  result.push(R.insert(0, \"data\", r));\n  burnups.filter((arr, i) => i < 500).forEach((arr, i) => result.push(R.insert(0, \"data\"+i, arr)));\n  //burnups.forEach((arr, i) => (arr.forEach((val, j) => burnups[i][j] = (i>0 ? val+0.5 : val))));\n  return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":976,"value":"dataWithHeaders = getDataWithHeader(result, burnups)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":63,"value":"historicalData = d3.csvParseRows(historicalDataText.replace(/,\\s*$/, \"\"));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":772,"value":"allData = simulate(numberOfSimultations)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4,"value":"import { number, slider, radio, date } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"import {R} from \"@itacirgabral/ramda\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":262,"value":"import {c3} from \"@mbostock/c3\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":30,"value":"d3 = require(\"d3@5\", \"d3-sankey@0.12\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":608,"value":"vegalite = require(\"vega-embed@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":577,"value":"style = html`\n<style>\n  .h1 {\n    font-size: 2em;\n    font-weight: bold;\n  }\n  .h2 {\n    font-size: 1.5em;\n    font-weight: bold;\n  }\n  .paragraph {\n    display: block;\n    margin-top: .5em;\n  }\n  .c3-region.green {\n    fill: green;\n  }\n  .c3-region.orange {\n    fill: orange;\n  }\n  .c3-region.red {\n    fill: red;\n  }\n  .c3-line {\n    stroke-width: 4px;\n  }\n</style>\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1194,"value":"pageBreak = `<div style=\"page-break-after: always;\"></div>`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}}
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