public void Forecast_8_issues()
{
const int N_RESOURCES = 3;
var issues = IssueRepository.Import().ToArray();
IEnumerable <Issue> Filter_issues(params string[] tags) => issues.Where(i => tags.All(t => i.Tags.Contains(t)));
int[] Get_cycle_times_in_days(IEnumerable <Issue> issues) => issues.Select(x => x.CycleTime.Days).ToArray();
var frontend_feature_cts = Get_cycle_times_in_days(Filter_issues("frontend", "feature"));
var backend_bug_cts = Get_cycle_times_in_days(Filter_issues("backend", "bug"));
var backend_feature_cts = Get_cycle_times_in_days(Filter_issues("backend", "feature"));
_testOutputHelper.WriteLine($"Events found: {frontend_feature_cts.Length}, {backend_bug_cts.Length}, {backend_feature_cts.Length}");
var sut = new SoftwareDeliverySimulation();
var simulationresult = sut.SimulateIssueDeliveryByResources(N_RESOURCES,
frontend_feature_cts,
frontend_feature_cts,
frontend_feature_cts,
backend_bug_cts,
backend_feature_cts,
backend_feature_cts,
backend_feature_cts,
backend_feature_cts
);
var distribution = Statistics.Distribution(simulationresult);
var deDE = new CultureInfo("de-DE");
foreach (var x in distribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void Forecast_based_on_all_issues()
{
var issues = IssueRepository.Import().ToArray();
var issueCycleTimes = issues.Select(x => x.CycleTime.Days).ToArray();
var distribution = Statistics.Distribution(issueCycleTimes);
var deDE = new CultureInfo("de-DE");
foreach (var x in distribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void Forecast_10_stories_for_all_issue_forecasts()
{
DateTime START_DATE = new DateTime(2019, 11, 18);
const int NUMBER_OF_STORIES = 10;
var issues = IssueRepository.Import().ToArray();
var sut = new SoftwareDeliverySimulation();
var simulationresult = sut.SimulateStoryDeliveryBasedOnThroughput(START_DATE, NUMBER_OF_STORIES, issues);
var distribution = Statistics.Distribution(simulationresult);
var deDE = new CultureInfo("de-DE");
foreach (var x in distribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void Forecast_8_issues()
{
DateTime START_DATE = new DateTime(2019, 11, 18);
const int NUMBER_OF_ISSUES = 8;
var issues = IssueRepository.Import().ToArray();
var tp = issues.BusinessDayThroughputs();
_testOutputHelper.WriteLine($"TP sum: {tp.Sum()}");
var sut = new SoftwareDeliverySimulation();
var simulationresult = sut.SimulateIssueDeliveryBasedOnThroughput(START_DATE, NUMBER_OF_ISSUES, tp);
var distribution = Statistics.Distribution(simulationresult);
var deDE = new CultureInfo("de-DE");
foreach (var x in distribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void Forecast_10_stories_with_single_issue_forecast()
{
DateTime START_DATE = new DateTime(2019, 11, 18);
const int NUMBER_OF_STORIES = 10;
var issues = IssueRepository.Import().ToArray();
var issuesPerStory = issues.IssuesPerStory();
var sut = new SoftwareDeliverySimulation();
var issueSimulationresult = sut.SimulateIssuesDerivedFromStories(
Enumerable.Range(1, NUMBER_OF_STORIES).Select(_ => issuesPerStory).ToArray()
);
var issueDistribution = Statistics.Distribution(issueSimulationresult);
_testOutputHelper.WriteLine("issues from stories forecast");
var deDE = new CultureInfo("de-DE");
foreach (var x in issueDistribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
_testOutputHelper.WriteLine("delivery forecast");
var number_of_issues = issueDistribution.First(x => x.percentile > 80.0).value;
_testOutputHelper.WriteLine($"Number of issues selected: {number_of_issues}");
var tp = issues.BusinessDayThroughputs();
var deliverySimulationresult = sut.SimulateIssueDeliveryBasedOnThroughput(START_DATE, number_of_issues, tp);
var distribution = Statistics.Distribution(deliverySimulationresult);
foreach (var x in distribution.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void Run()
{
const int INITIAL_NUMBER_OF_ISSUES = 5;
var sut = new Forecasting();
var issues = IssueRepository.Import().ToList();
var cts = issues.Select(x => x.CycleTime.Days).ToArray();
var ctsDistribution = Statistics.Distribution(cts);
Export("CTs 2019-11-06.csv", ctsDistribution);
var tps = issues.BusinessDayThroughputs();
var tpsDistribution = Statistics.Distribution(tps);
Export("TPs 2019-11-06.csv", tpsDistribution);
// initial forecast
var numberOfIssues = INITIAL_NUMBER_OF_ISSUES;
var fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 6), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 6), fc);
// updating forecast after first issue got delivered
issues.Add(new Issue(new DateTime(2019, 11, 6), new DateTime(2019, 11, 7), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 7), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 7), fc);
issues.Add(new Issue(new DateTime(2019, 11, 7), new DateTime(2019, 11, 8), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 8), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 8), fc);
issues.Add(new Issue(new DateTime(2019, 11, 6), new DateTime(2019, 11, 11), null, null, false));
numberOfIssues += -1 + 2;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 11), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 11), fc);
issues.Add(new Issue(new DateTime(2019, 11, 11), new DateTime(2019, 11, 13), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 13), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 13), fc);
issues.Add(new Issue(new DateTime(2019, 11, 8), new DateTime(2019, 11, 14), null, null, false));
numberOfIssues += -1 + 1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 14), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 14), fc);
issues.Add(new Issue(new DateTime(2019, 11, 11), new DateTime(2019, 11, 15), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 15), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 15), fc);
issues.Add(new Issue(new DateTime(2019, 11, 13), new DateTime(2019, 11, 18), null, null, false));
numberOfIssues += -1 + 2;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 18), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 18), fc);
issues.Add(new Issue(new DateTime(2019, 11, 15), new DateTime(2019, 11, 19), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 19), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 19), fc);
issues.Add(new Issue(new DateTime(2019, 11, 18), new DateTime(2019, 11, 20), null, null, false));
numberOfIssues += -1;
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 20), numberOfIssues, issues.ToArray());
Export(new DateTime(2019, 11, 20), fc);
issues.Add(new Issue(new DateTime(2019, 11, 18), new DateTime(2019, 11, 21), null, null, false));
numberOfIssues += -1;
Assert.Equal(0, numberOfIssues);
// rear mirror wisdom
fc = sut.WhenWillTheIssuesBeDone(new DateTime(2019, 11, 6), INITIAL_NUMBER_OF_ISSUES + 5, issues.ToArray());
File.Move("2019-11-06.csv", "2019-11-06-v1.csv", true);
Export(new DateTime(2019, 11, 6), fc);
File.Move("2019-11-06.csv", "2019-11-06-v2.csv", true);
cts = issues.Select(x => x.CycleTime.Days).ToArray();
ctsDistribution = Statistics.Distribution(cts);
Export("CTs 2019-11-21.csv", ctsDistribution);
tps = issues.BusinessDayThroughputs();
tpsDistribution = Statistics.Distribution(tps);
Export("TPs 2019-11-21.csv", tpsDistribution);
}
