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test/stats/statcmp.js

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+/* eslint-disable import/no-commonjs */
+
+import { createRequire } from "module";
+import fs from "fs";
+
+const require = createRequire(import.meta.url);
+const ttest = require("ttest");
+
+const VALID_GROUP_BYS = ["browser", "pdf", "page", "round", "stat"];
+
+function parseOptions() {
+  const yargs = require("yargs")
+    .usage(
+      "Compare the results of two stats files.\n" +
+        "Usage:\n  $0 <BASELINE> <CURRENT> [options]"
+    )
+    .demand(2)
+    .string(["groupBy"])
+    .describe(
+      "groupBy",
+      "How statistics should grouped. Valid options: " +
+        VALID_GROUP_BYS.join(" ")
+    )
+    .default("groupBy", "browser,stat");
+  const result = yargs.argv;
+  result.baseline = result._[0];
+  result.current = result._[1];
+  if (result.groupBy) {
+    result.groupBy = result.groupBy.split(/[;, ]+/);
+  }
+  return result;
+}
+
+function group(stats, groupBy) {
+  const vals = [];
+  for (const curStat of stats) {
+    const keyArr = [];
+    for (const entry of groupBy) {
+      keyArr.push(curStat[entry]);
+    }
+    const key = keyArr.join(",");
+    (vals[key] ||= []).push(curStat.time);
+  }
+  return vals;
+}
+
+/*
+ * Flatten the stats so that there's one row per stats entry.
+ * Also, if results are not grouped by 'stat', keep only 'Overall' results.
+ */
+function flatten(stats) {
+  let rows = [];
+  stats.forEach(function (curStat) {
+    curStat.stats.forEach(function (s) {
+      rows.push({
+        browser: curStat.browser,
+        page: curStat.page,
+        pdf: curStat.pdf,
+        round: curStat.round,
+        stat: s.name,
+        time: s.end - s.start,
+      });
+    });
+  });
+  // Use only overall results if not grouped by 'stat'
+  if (!options.groupBy.includes("stat")) {
+    rows = rows.filter(s => s.stat === "Overall");
+  }
+  return rows;
+}
+
+function pad(s, length, dir /* default: 'right' */) {
+  s = "" + s;
+  const spaces = new Array(Math.max(0, length - s.length + 1)).join(" ");
+  return dir === "left" ? spaces + s : s + spaces;
+}
+
+function mean(array) {
+  return array.reduce((a, b) => a + b, 0) / array.length;
+}
+
+/* Comparator for row key sorting. */
+function compareRow(a, b) {
+  a = a.split(",");
+  b = b.split(",");
+  for (let i = 0; i < Math.min(a.length, b.length); i++) {
+    const intA = parseInt(a[i], 10);
+    const intB = parseInt(b[i], 10);
+    const ai = isNaN(intA) ? a[i] : intA;
+    const bi = isNaN(intB) ? b[i] : intB;
+    if (ai < bi) {
+      return -1;
+    }
+    if (ai > bi) {
+      return 1;
+    }
+  }
+  return 0;
+}
+
+/*
+ * Dump various stats in a table to compare the baseline and current results.
+ * T-test Refresher:
+ * If I understand t-test correctly, p is the probability that we'll observe
+ * another test that is as extreme as the current result assuming the null
+ * hypothesis is true. P is NOT the probability of the null hypothesis. The null
+ * hypothesis in this case is that the baseline and current results will be the
+ * same. It is generally accepted that you can reject the null hypothesis if the
+ * p-value is less than 0.05. So if p < 0.05 we can reject the results are the
+ * same which doesn't necessarily mean the results are faster/slower but it can
+ * be implied.
+ */
+function stat(baseline, current) {
+  const baselineGroup = group(baseline, options.groupBy);
+  const currentGroup = group(current, options.groupBy);
+
+  const keys = Object.keys(baselineGroup);
+  keys.sort(compareRow);
+
+  const labels = options.groupBy.slice(0);
+  labels.push("Count", "Baseline(ms)", "Current(ms)", "+/-", "% ");
+  if (ttest) {
+    labels.push("Result(P<.05)");
+  }
+  const rows = [];
+  // collect rows and measure column widths
+  const width = labels.map(s => s.length);
+  rows.push(labels);
+  for (const key of keys) {
+    const baselineMean = mean(baselineGroup[key]);
+    const currentMean = mean(currentGroup[key]);
+    const row = key.split(",");
+    row.push(
+      "" + baselineGroup[key].length,
+      "" + Math.round(baselineMean),
+      "" + Math.round(currentMean),
+      "" + Math.round(currentMean - baselineMean),
+      ((100 * (currentMean - baselineMean)) / baselineMean).toFixed(2)
+    );
+    if (ttest) {
+      const p =
+        baselineGroup[key].length < 2
+          ? 1
+          : ttest(baselineGroup[key], currentGroup[key]).pValue();
+      if (p < 0.05) {
+        row.push(currentMean < baselineMean ? "faster" : "slower");
+      } else {
+        row.push("");
+      }
+    }
+    for (let i = 0; i < row.length; i++) {
+      width[i] = Math.max(width[i], row[i].length);
+    }
+    rows.push(row);
+  }
+
+  // add horizontal line
+  const hline = width.map(w => new Array(w + 1).join("-"));
+  rows.splice(1, 0, hline);
+
+  // print output
+  console.log("-- Grouped By " + options.groupBy.join(", ") + " --");
+  const groupCount = options.groupBy.length;
+  for (const row of rows) {
+    for (let i = 0; i < row.length; i++) {
+      row[i] = pad(row[i], width[i], i < groupCount ? "right" : "left");
+    }
+    console.log(row.join(" | "));
+  }
+}
+
+function main() {
+  let baseline, current;
+  try {
+    const baselineFile = fs.readFileSync(options.baseline).toString();
+    baseline = flatten(JSON.parse(baselineFile));
+  } catch (e) {
+    console.log('Error reading file "' + options.baseline + '": ' + e);
+    process.exit(0);
+  }
+  try {
+    const currentFile = fs.readFileSync(options.current).toString();
+    current = flatten(JSON.parse(currentFile));
+  } catch (e) {
+    console.log('Error reading file "' + options.current + '": ' + e);
+    process.exit(0);
+  }
+  stat(baseline, current);
+}
+
+const options = parseOptions();
+main();