*       mutable objects as elements at all, as many users may expect your "immutable" object to be
 *       <i>deeply</i> immutable.
 * </ul>
 *
 * <h4>Performance notes</h4>
 *
 * <ul>
 *   <li>Implementations can be generally assumed to prioritize memory efficiency, then speed of
 *       access, and lastly speed of creation.

   *
   * <p><b>Note:</b> if you won't be adding any elements to the list, use {@link ImmutableList#of()}
   * instead.
   *
   * <p><b>Performance note:</b> {@link ArrayList} and {@link java.util.ArrayDeque} consistently
   * outperform {@code LinkedList} except in certain rare and specific situations. Unless you have

    }

    def "matches daemon process started by performance test on Windows"() {

Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.
This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**

import japicmp.model.JApiMethod
import javassist.bytecode.SourceFileAttribute
import java.io.File


/**
 * Repository of sources for binary compatibility checks.
 *
 * `WARN` Holds resources open for performance, must be closed after use.
 */
class BinaryCompatibilityRepository internal constructor(

    private
    val sources: SourcesRepository

) : AutoCloseable {

    companion object {

      label: Affected product area
      options:
      - label: "Ambient"
      - label: "Docs"
      - label: "Dual Stack"
      - label: "Installation"
      - label: "Networking"
      - label: "Performance and Scalability"
      - label: "Extensions and Telemetry"
      - label: "Security"
      - label: "Test and Release"
      - label: "User Experience"
      - label: "Developer Infrastructure"
      - label: "Upgrade"

regular | standard | 97 | 3.6MB
regular | jstream | 175 | 2.1MB
large | standard | 92 | 305MB
large | jstream | 404 | 69MB

In a real world scenario, including initialization and reader overhead from varying blob sizes, performance can be expected as below:

 *
 * We perform this filtering, since if we simply include all external dependencies, regardless of whether
 * they are already loaded transitively, there is a measurable performance impact during module-loading.
 */
fun computeExternalDependenciesNotAccessibleFromProjectDependencies(
    rootComponent: ResolvedComponentResult,
    rootVariant: ResolvedVariantResult
): Set<ComponentIdentifier> {

        if ((s.length() > 1) && s.startsWith("0")) {
            return null;
        }
        return tryParseInt(s);
    }

    private static Integer tryParseInt(String s) {
        // for performance, check digits instead of relying later on catching NumberFormatException
        if (!isDigits(s)) {
            return null;
        }

        try {
            long longValue = Long.parseLong(s);

    }

    public MaxCodePoint(String userFriendly) {
      value = decode(userFriendly);
    }
  }

  /**
   * The default values of maxCodePoint below provide pretty good performance models of different
   * kinds of common human text.
   *
   * @see MaxCodePoint#decode
   */
  @Param({"0x80", "0x90", "0x100", "0x800", "0x10000", "0x10ffff"})
  MaxCodePoint maxCodePoint;

  @Param({"16384"})