public void Story_refinement()
{
var historicalStoryRefinementsA = new[] { 1, 2, 3 };
var historicalStoryRefinementsB = new[] { 10, 20, 30 };
var randomNumbers = new Queue <int>(new[] { 2, 1, 0, 2, 1, 0 });
var sut = new SoftwareDeliverySimulation(3, maxNumber => randomNumbers.Dequeue());
var result = sut.SimulateIssuesDerivedFromStories(historicalStoryRefinementsA, historicalStoryRefinementsB);
Assert.Equal(new[] { 23, 31, 12 }, result);
}
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)}");
}
}