label: "Orthogonality: How does this change interact or overlap with existing features?"
      description: "Is the goal of this change a performance improvement? If so, what quantifiable improvement should we expect? How would we measure it?"
    validations:
      required: false

  - type: textarea
    id: learning-curve
    attributes:
      label: "Would this change make Go easier or harder to learn, and why?"

  - type: textarea

  private void weaklyReferenceQueue() {
    frq = new FinalizableReferenceQueue();
    queueReference = new WeakReference<>(frq.queue);

    /*
     * Queue and clear a reference for good measure. We test later on that
     * the finalizer thread stopped, but we should test that it actually
     * started first.
     */
    reference =
        new FinalizableWeakReference<Object>(new Object(), frq) {

    @Test
    public void test_integrationScenarios() {
        // Test realistic scenarios combining multiple methods
        String memoryLog1 = MemoryUtil.getMemoryUsageLog();

        // Create some objects and measure their sizes
        List<String> testData = new ArrayList<>();
        for (int i = 0; i < 100; i++) {
            testData.add("test data " + i);
        }

        long listSize = MemoryUtil.sizeOf(testData);

      if (contentType != null) {
        sink
          .writeUtf8("Content-Type: ")
          .writeUtf8(contentType.toString())
          .write(CRLF)
      }

      // We can't measure the body's size without the sizes of its components.
      val contentLength = body.contentLength()
      if (contentLength == -1L && countBytes) {
        byteCountBuffer!!.clear()
        return -1L
      }

   * @param offset the offset into the array at which to start
   * @return the value found in the array in the form of a long
   */
  static int load32(byte[] source, int offset) {
    // TODO(user): Measure the benefit of delegating this to LittleEndianBytes also.
    return (source[offset] & 0xFF)
        | ((source[offset + 1] & 0xFF) << 8)
        | ((source[offset + 2] & 0xFF) << 16)

          if ((hash1 & (1 << k)) == (hash2 & (1 << k))) {
            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        assertThat(prob).isWithin(epsilon).of(0.50d);

        String testField = "field999";

        // Warm up
        for (int i = 0; i < 100; i++) {
            queryCommand.isSearchField(testField);
        }

        // Measure Set-based lookup
        long startTime = System.nanoTime();
        for (int i = 0; i < 10000; i++) {
            queryCommand.isSearchField(testField);
        }
        long duration = System.nanoTime() - startTime;

    
    SmbFile file = new SmbFile("smb://server/share/largefile.dat", context);
    
    // Measure single channel throughput
    long singleChannelTime = measureReadTime(file, false);
    
    // Measure multi-channel throughput
    long multiChannelTime = measureReadTime(file, true);
    
    // Multi-channel should be faster

 * <p>A high-quality hash function strives for some subset of the following virtues:
 *
 * <ul>
 *   <li><b>collision-resistant:</b> while the definition above requires making at least <i>some</i>
 *       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision

        String testField = "field999";

        // Warm up
        for (int i = 0; i < 100; i++) {
            queryFieldConfig.isSortField(testField);
        }

        // Measure Set-based lookup (current implementation)
        long setStartTime = System.nanoTime();
        for (int i = 0; i < 10000; i++) {
            queryFieldConfig.isSortField(testField);
        }