// Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  @Param HashFunctionEnum hashFunctionEnum;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    random.nextBytes(testBytes);
  }

  @Benchmark

        }
        return root;
      }
    },
    RANDOM {
      /**
       * Generates a tree with topology selected uniformly at random from the topologies of binary
       * trees of the specified size.
       */
      @Override
      Optional<BinaryNode> createTree(int size, Random rng) {
        int[] keys = new int[size];
        for (int i = 0; i < size; i++) {

  int[] queries = new int[ARRAY_SIZE];

  @BeforeExperiment
  void setUp() {
    Random random = new Random();
    multisets.clear();
    for (int i = 0; i < ARRAY_SIZE; i++) {
      HashMultiset<Integer> multiset = HashMultiset.<Integer>create();
      multisets.add(multiset);
      queries[i] = random.nextInt();
      multiset.add(queries[i]);
    }
  }

  @Benchmark
  int add(int reps) {

    return new String(result);
  }

  private static String newTestString(Random random, BitSet bitSet, int percentMatching) {
    final String allMatchingChars = allMatchingChars(bitSet);
    final char[] result = new char[STRING_LENGTH];
    // Fill with matching chars.
    for (int i = 0; i < result.length; i++) {
      result[i] = allMatchingChars.charAt(random.nextInt(allMatchingChars.length()));
    }

    if (slice && bytes.length > 0) {
      // test a random slice() of the ByteSource
      Random random = new Random();
      byte[] expected = factory.getExpected(bytes);
      // if expected.length == 0, off has to be 0 but length doesn't matter--result will be empty
      int off = expected.length == 0 ? 0 : random.nextInt(expected.length);
      int len = expected.length == 0 ? 4 : random.nextInt(expected.length - off);

 * only a secondary goal.
 *
 * @author Kurt Alfred Kluever
 */
public class HashCodeBenchmark {

  // Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

  @Param({"1000", "100000"})
  private int size;

  @Param WhereToDiffer whereToDiffer;

  @Param EqualsImplementation equalsImpl;

 * only a secondary goal.
 *
 * @author Kurt Alfred Kluever
 */
public class HashCodeBenchmark {

  // Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

  @Param({"1000", "100000"})
  private int size;

  @Param WhereToDiffer whereToDiffer;

  @Param EqualsImplementation equalsImpl;

    }
  }

  // Use a constant seed for all of the benchmarks to ensure apples to apples comparisons.
  private static final int RANDOM_SEED = new Random().nextInt();

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    new Random(RANDOM_SEED).nextBytes(testBytes);
  }

  @Benchmark
  byte hashing(int reps) {
    byte result = 0x01;

      exponent[i] = randomExponent();
      factorial[i] = RANDOM_SOURCE.nextInt(50);
      binomial[i] = RANDOM_SOURCE.nextInt(factorial[i] + 1);
      positive[i] = randomPositiveBigInteger(Integer.SIZE - 1).intValue();
      nonnegative[i] = randomNonNegativeBigInteger(Integer.SIZE - 1).intValue();
      ints[i] = RANDOM_SOURCE.nextInt();
    }
  }

  @Benchmark
  int pow(int reps) {
    int tmp = 0;

  }

  @Test
  public void undirectedGraph() {
    testGraphMutation(GraphBuilder.undirected());
  }

  private static void testGraphMutation(GraphBuilder<? super Integer> graphBuilder) {
    Random gen = new Random(42); // Fixed seed so test results are deterministic.

    for (int trial = 0; trial < NUM_TRIALS; ++trial) {
      MutableGraph<Integer> graph = graphBuilder.allowsSelfLoops(true).build();