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Remove dependencies on commons-math3 and ssj by implementing simple linear regression directly, replacing the build duration distribution chart with a histogram, and deleting the smoothed trend lines in the test history charts #639

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merged 4 commits into from
Aug 21, 2024
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Remove dependencies on commons-math3 and ssj by implementing simple linear regression directly, replacing the build duration distribution chart with a histogram, and deleting the smoothed trend lines in the test history charts #639

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a5fef6a
Implement simple linear regression directly and drop commons-math3 de…
dwnusbaum Aug 20, 2024
23c682e
Turn build duration distribution chart into a histogram
dwnusbaum Aug 20, 2024
dc85191
Remove ssj dependency and spline trend lines when over 200 tests are …
dwnusbaum Aug 20, 2024
88552fc
Remove commented-out line of code
dwnusbaum Aug 20, 2024
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17 changes: 0 additions & 17 deletions pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -59,18 +59,6 @@
</dependencyManagement>

<dependencies>
<dependency>
<groupId>ca.umontreal.iro.simul</groupId>
<artifactId>ssj</artifactId>
<version>3.3.2</version>
<!-- Dependencies are unused, provided by Jenkins core, have unusual licenses, unclear artifact ownership, and/or are obsolete. We only use SmoothingCubicSpline, which does not require dependencies. -->
<exclusions>
<exclusion>
<groupId>*</groupId>
<artifactId>*</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>com.pivovarit</groupId>
<artifactId>parallel-collectors</artifactId>
Expand All @@ -96,11 +84,6 @@
<groupId>io.jenkins.plugins</groupId>
<artifactId>plugin-util-api</artifactId>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-math3</artifactId>
<version>3.6.1</version>
</dependency>
<dependency>
<groupId>org.jenkins-ci.plugins</groupId>
<artifactId>display-url-api</artifactId>
Expand Down
155 changes: 58 additions & 97 deletions src/main/java/hudson/tasks/junit/History.java
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Expand Up @@ -46,11 +46,9 @@
import java.util.concurrent.atomic.AtomicInteger;
import java.util.stream.Collectors;
import jenkins.util.SystemProperties;
import org.apache.commons.math3.stat.regression.SimpleRegression;
import org.kohsuke.accmod.Restricted;
import org.kohsuke.accmod.restrictions.NoExternalUse;
import org.kohsuke.stapler.bind.JavaScriptMethod;
import umontreal.ssj.functionfit.SmoothingCubicSpline;

/**
* History of {@link hudson.tasks.test.TestObject} over time.
Expand Down Expand Up @@ -209,16 +207,6 @@
0,
0,
roundMul); // "--blue"
createSplineTrend(
series,
history,
lrX,
lrY,
"Smooth of " + durationStr,
"rgba(120, 50, 255, 0.5)",
0,
0,
roundMul); // "--indigo"
}
root.set("series", series);
root.set("domainAxisLabels", domainAxisLabels);
Expand Down Expand Up @@ -249,12 +237,9 @@
return;
}

SimpleRegression sr = new SimpleRegression(true);
for (int i = 0; i < lrX.length; i++) {
sr.addData(lrX[i], lrY[i]);
}
double intercept = sr.getIntercept();
double slope = sr.getSlope();
double[] cs = SimpleLinearRegression.coefficients(lrX, lrY);
double intercept = cs[0];
double slope = cs[1];

ObjectNode lrSeries = MAPPER.createObjectNode();
series.add(lrSeries);
Expand Down Expand Up @@ -283,51 +268,6 @@
}
}

private void createSplineTrend(
ArrayNode series,
List<HistoryTestResultSummary> history,
double[] lrX,
double[] lrY,
String title,
String color,
int xAxisIndex,
int yAxisIndex,
double roundMul) {
if (history.size() < 200) {
return;
}
double rho = Math.min(1.0, 1.0 / Math.max(1, (history.size() / 2)));
if (rho > 0.75) {
return; // Too close to linear
}
SmoothingCubicSpline scs = new SmoothingCubicSpline(lrX, lrY, 0.001);
ObjectNode lrSeries = MAPPER.createObjectNode();
series.add(lrSeries);
lrSeries.put("name", title);
lrSeries.put("preferScreenOrient", "landscape");
lrSeries.put("type", "line");
lrSeries.put("symbol", "circle");
lrSeries.put("symbolSize", 0);
lrSeries.put("xAxisIndex", xAxisIndex);
lrSeries.put("yAxisIndex", yAxisIndex);
ArrayNode lrData = MAPPER.createArrayNode();
lrSeries.set("data", lrData);
ObjectNode lrStyle = MAPPER.createObjectNode();
lrSeries.set("itemStyle", lrStyle);
lrStyle.put("color", color);
ObjectNode lrAreaStyle = MAPPER.createObjectNode();
lrSeries.set("areaStyle", lrAreaStyle);
lrAreaStyle.put("color", "rgba(0, 0, 0, 0)");

if (roundMul < 10.0) {
roundMul = 10.0;
}
for (int index = 0; index < history.size(); ++index) {
// Use float to reduce JSON size.
lrData.add((float) (Math.round(scs.evaluate(index) * roundMul) / roundMul));
}
}

static boolean EXTRA_GRAPH_MATH_ENABLED =
Boolean.parseBoolean(System.getProperty(History.class.getName() + ".EXTRA_GRAPH_MATH_ENABLED", "true"));

Expand Down Expand Up @@ -468,8 +408,6 @@
1,
1,
10.0); // "--dark-blue"
createSplineTrend(
series, history, lrX, lrY, "Smooth of Passed", "rgba(255, 50, 255, 0.5)", 1, 1, 10.0); // "--purple"
}

root.set("series", series);
Expand All @@ -483,24 +421,16 @@
private ObjectNode computeDistributionJson(List<HistoryTestResultSummary> history) {
ObjectNode root = MAPPER.createObjectNode();
ArrayNode series = MAPPER.createArrayNode();
ArrayNode domainAxisLabels = MAPPER.createArrayNode();

ObjectNode durationSeries = MAPPER.createObjectNode();
durationSeries.put("name", "Build Count");
durationSeries.put("type", "line");
durationSeries.put("symbol", "circle");
durationSeries.put("symbolSize", "0");
durationSeries.put("sampling", "lttb");
durationSeries.put("type", "bar");
durationSeries.put("barWidth", "99%");
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Slightly less than 100% so you see a border between each bar.

ArrayNode durationData = MAPPER.createArrayNode();
durationSeries.set("data", durationData);
ObjectNode durationStyle = MAPPER.createObjectNode();
durationSeries.set("itemStyle", durationStyle);
durationStyle.put("color", "--success-color"); // "rgba(50, 200, 50, 0.8)");
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@dwnusbaum dwnusbaum Aug 20, 2024 •
edited
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This color is a bit ugly now that it is not just a simple line in case anyone has any recommendations. I think ideally this would be a stacked histogram chart with each build result shown in a different color.

durationSeries.put("stack", "stacked");
ObjectNode durAreaStyle = MAPPER.createObjectNode();
durationSeries.set("areaStyle", durAreaStyle);
durAreaStyle.put("color", "rgba(0,0,0,0)");
durationSeries.put("smooth", true);
series.add(durationSeries);

double maxDuration = 0, minDuration = Double.MAX_VALUE;
Expand All @@ -512,22 +442,20 @@
minDuration = h.getDuration();
}
}
double extraDuration = Math.max(0.001, (maxDuration - minDuration) * 0.05);
minDuration = Math.max(0.0, minDuration - extraDuration);
maxDuration = maxDuration + extraDuration;
int[] counts = new int[100];
int smoothBuffer = 2;
double[] lrX = new double[counts.length + smoothBuffer * 2 + 1];
double[] lrY = new double[counts.length + smoothBuffer * 2 + 1];
double scale = maxDuration - minDuration;
double step = scale / counts.length;
// double extraDuration = Math.max(0.001, (maxDuration - minDuration) * 0.05);
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minDuration = Math.max(0.0, minDuration);
int buckets = 50;
double[] lrX = new double[buckets];
double[] lrY = new double[buckets];
double domain = maxDuration - minDuration;
double step = domain / buckets;
for (HistoryTestResultSummary h : history) {
int idx = smoothBuffer + (int) Math.round((h.getDuration() - minDuration) / step);
int idx = (int) Math.round((h.getDuration() - minDuration) / step);
int idx2 = Math.max(0, Math.min(idx, lrY.length - 1));
lrY[idx2]++;
}
for (int i = 0; i < lrY.length; ++i) {
lrX[i] = ((minDuration + (maxDuration - minDuration) / lrY.length * i) / scale * 100.0);
lrX[i] = minDuration + step * (i + 0.5);
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Offset the X values so they are in the middle of the bucket. We are no longer scaling them into the range 0-1 because we are using them directly in our data series.

}

ObjectNode xAxis = MAPPER.createObjectNode();
Expand All @@ -551,24 +479,25 @@
mul = 1.0d / 3600.0d;
roundMul = 100.0;
}
xAxis.put("min", (float) (mul * lrX[0] - (step * mul * 0.5)));
xAxis.put("max", (float) (mul * lrX[lrX.length - 1] + (step * mul * 0.5)));
xAxis.put("interval", (float) (step * mul));
xAxis.put("roundingFactor", (float) roundMul);

int maxBuilds = 0;
SmoothingCubicSpline scs = new SmoothingCubicSpline(lrX, lrY, 0.1);
int smoothPts = counts.length * 4;
double k = (double) counts.length / smoothPts;
final double splineRoundMul = 1000.0;
for (double z = minDuration; z < maxDuration; z += step * k) {
double v = Math.round(splineRoundMul * Math.max(0.0, scs.evaluate(z / scale * 100.0))) / splineRoundMul;
durationData.add((float) v);
maxBuilds = Math.max(maxBuilds, (int) Math.ceil(v));
// Use float for smaller JSONs.
domainAxisLabels.add((float) (Math.round(mul * z * roundMul) / roundMul));
for (int i = 0; i < lrY.length; i++) {
double scaledX = mul * lrX[i];
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We no longer round our X values in the backend (z in the old code), because our axis type is now value instead of category, and so if we round here our bars will have weird gaps and not be contiguous.

double y = lrY[i];
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Nothing fancy with the Y values anymore, this is just the frequency of build durations in this bucket. We can probably make lrY an int[] now.

ArrayNode data = MAPPER.createArrayNode();
data.add((float) scaledX);
data.add((float) y);
durationData.add(data);
maxBuilds = Math.max(maxBuilds, (int) Math.ceil(y));
}

root.set("series", series);
root.put("integerRangeAxis", true);
root.put("domainAxisItemName", "Number of Builds");
root.set("domainAxisLabels", domainAxisLabels);
root.set("xAxis", xAxis);
if (maxBuilds >= 10) {
root.put("rangeMax", maxBuilds);
Expand Down Expand Up @@ -813,4 +742,36 @@
return defaultValue;
}
}

// https://en.wikipedia.org/wiki/Simple_linear_regression
static class SimpleLinearRegression {

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static double[] coefficients(double[] xs, double[] ys) {
int n = xs.length;
if (n < 2) {
throw new IllegalArgumentException("At least two data points are required, but got: " + xs.length);
}
if (xs.length != ys.length) {
throw new IllegalArgumentException("Array lengths do not match:" + xs.length + " vs " + ys.length);
}
double sumX = 0;
double sumY = 0;
double sumXX = 0;
double sumXY = 0;
for (int i = 0; i < n; i++) {
double x = xs[i];
double y = ys[i];
sumX += x;
sumY += y;
sumXX += x * x;
sumXY += x * y;
}
if (Math.abs(sumXX) < 10 * Double.MIN_VALUE) {
// Avoid returning +/- infinity in case the x values are too close together.
return new double[] {Double.NaN, Double.NaN};
}
double slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
double intercept = sumY / n - slope / n * sumX;
return new double[] {intercept, slope};
}
}
}
13 changes: 9 additions & 4 deletions src/main/resources/hudson/tasks/junit/History/history.js
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Expand Up @@ -323,12 +323,16 @@ function onBuildIntervalChange(selectObj) {
top: '20%',
},
xAxis: {
type: 'category',
boundaryGap: false,
type: 'value',
axisLabel: {
color: textColor
color: textColor,
formatter: function(value) {
return Math.round(value * model.distribution.xAxis.roundingFactor) / model.distribution.xAxis.roundingFactor;
Comment on lines +329 to +330
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Round the labels in the frontend.

}
},
data: model.distribution.domainAxisLabels,
min: model.distribution.xAxis.min,
max: model.distribution.xAxis.max,
minInterval: model.distribution.xAxis.interval,
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What I really wanted was to be able to tell echarts to pick an interval that is a multiple of model.distribution.xAxis.interval, and then let it automatically choose which multiple based on screen and label size, but I didn't see any way to do it.

If you set interval: model.distribution.xAxis.interval, then you get one grid line per bucket, which is nice, but the labels are unreadably close to each other and I couldn't find a way to fix that dynamically. You can set interval: model.distribution.xAxis.interval * 2 and things look fine on "normalish" laptop screen size, but again it doesn't scale at all if you shrink your screen size.

I also wanted to have dynamic minorTick support, e.g. if we let echarts pick the interval, could we at least set up the minor ticks to align with the buckets? Unfortunately I don't think so, you can only control how many minor ticks you want between each major tick, e.g. if you specify interval: model.distribution.xAxis.interval * 3, you could specify minorTick { show: true, splitNumber: 3 }, but again that doesn't adjust based on the data or screen size at all.

name: model.distribution.xAxis.name,
nameLocation: 'middle',
nameGap: 26,
Expand All @@ -343,6 +347,7 @@ function onBuildIntervalChange(selectObj) {
axisLabel: {
color: textColor
},
minInterval: model.result.integerRangeAxis ? 1 : null,
name: 'Build Count',
nameLocation: 'middle',
nameGap: 60,
Expand Down
73 changes: 73 additions & 0 deletions src/test/java/hudson/tasks/junit/SimpleLinearRegressionTest.java
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@@ -0,0 +1,73 @@
package hudson.tasks.junit;

import static org.hamcrest.MatcherAssert.assertThat;
import static org.hamcrest.Matchers.closeTo;
import static org.hamcrest.Matchers.containsString;
import static org.hamcrest.Matchers.notANumber;
import static org.junit.Assert.assertThrows;

import hudson.tasks.junit.History.SimpleLinearRegression;
import org.junit.Test;

public class SimpleLinearRegressionTest {

@Test
public void smokes() {
// Results checked in Excel.
double[] xs = {2, 3, 4, 5, 6, 8, 10, 11};
double[] ys = {21.05, 23.51, 24.23, 27.71, 30.86, 45.85, 52.12, 55.98};
double[] cs = SimpleLinearRegression.coefficients(xs, ys);
assertThat(cs[0], closeTo(9.4763, 0.0001));
assertThat(cs[1], closeTo(4.1939, 0.0001));

xs = new double[] {1.47, 1.5, 1.52, 1.55, 1.57, 1.6, 1.63, 1.65, 1.68, 1.7, 1.73, 1.75, 1.78, 1.8, 1.83};
ys = new double[] {
52.21, 53.12, 54.48, 55.84, 57.2, 58.57, 59.93, 61.29, 63.11, 64.47, 66.28, 68.1, 69.92, 72.19, 74.46
};
cs = SimpleLinearRegression.coefficients(xs, ys);
assertThat(cs[0], closeTo(-39.0620, 0.0001));
assertThat(cs[1], closeTo(61.2722, 0.0001));
}

@Test
public void requires2DataPoints() {
var t = assertThrows(
IllegalArgumentException.class,
() -> SimpleLinearRegression.coefficients(new double[0], new double[0]));
assertThat(t.getMessage(), containsString("At least two data points are required"));
t = assertThrows(
IllegalArgumentException.class,
() -> SimpleLinearRegression.coefficients(new double[1], new double[1]));
assertThat(t.getMessage(), containsString("At least two data points are required"));
double[] cs = SimpleLinearRegression.coefficients(new double[] {0.0, 1.0}, new double[] {1.0, 1.0});
assertThat(cs[0], closeTo(1.0, 0.001));
assertThat(cs[1], closeTo(0, 0.001));
}

@Test
public void requiresArraysWithSameLength() {
var t = assertThrows(
IllegalArgumentException.class,
() -> SimpleLinearRegression.coefficients(new double[3], new double[4]));
assertThat(t.getMessage(), containsString("Array lengths do not match"));
t = assertThrows(
IllegalArgumentException.class,
() -> SimpleLinearRegression.coefficients(new double[4], new double[3]));
assertThat(t.getMessage(), containsString("Array lengths do not match"));
}

@Test
public void returnsNanIfXValuesDoNotVaryEnough() {
double[] xs = {Double.MIN_VALUE, 1e162 * Double.MIN_VALUE};
double[] ys = {0.0, 1.0};
double[] cs = SimpleLinearRegression.coefficients(xs, ys);
assertThat(cs[0], notANumber());
assertThat(cs[1], notANumber());

xs = new double[] {Double.MIN_VALUE, 1e163 * Double.MIN_VALUE};
ys = new double[] {0.0, 1.0};
cs = SimpleLinearRegression.coefficients(xs, ys);
assertThat(cs[0], closeTo(0.0, 0.001));
assertThat(cs[1], closeTo(2.0e160, 1e159));
}
}