assertArrayEquals(largeTestData, decompressed);
    }

    @Test
    @DisplayName("Test compression ratio estimation")
    public void testCompressionRatioEstimation() {
        double ratio = compressionService.estimateCompressionRatio(testData, CompressionService.COMPRESSION_LZ77);
        assertTrue(ratio >= 0.0 && ratio <= 1.0);

     */
    int[] getSupportedAlgorithms();

    /**
     * Estimates the compression ratio for the given data and algorithm.
     * This can be used to decide whether compression is worthwhile.
     *
     * @param data the data to analyze
     * @param algorithm the compression algorithm
     * @return estimated compression ratio (0.0 to 1.0, where 0.5 means 50% size reduction)
     */

            // Timing differences should be minimal (within tolerance)
            assertTrue(timingRatio < TIMING_TOLERANCE,
                    String.format(
                            "Timing attack vulnerability detected: timing ratio %.3f exceeds tolerance %.3f "
                                    + "(start: %d ns, middle: %d ns, end: %d ns)",
                            timingRatio, TIMING_TOLERANCE, timeStart, timeMiddle, timeEnd));
        } finally {

  }

  /** Returns the number of times {@link Cache} lookup methods have returned a cached value. */
  public long hitCount() {
    return hitCount;
  }

  /**
   * Returns the ratio of cache requests which were hits. This is defined as {@code hitCount /
   * requestCount}, or {@code 1.0} when {@code requestCount == 0}. Note that {@code hitRate +
   * missRate =~ 1.0}.
   */
  public double hitRate() {

        a full-time Guava team member. [Feedback](https://stackoverflow.com/a/4543114) from our
        users indicates that they really appreciate Guava's high power-to-weight ratio. It's
        important to us to keep Guava as easy to use and understand as we can. That means boiling
        features down to compact but powerful abstractions, and controlling feature bloat carefully.

        a full-time Guava team member. [Feedback](https://stackoverflow.com/a/4543114) from our
        users indicates that they really appreciate Guava's high power-to-weight ratio. It's
        important to us to keep Guava as easy to use and understand as we can. That means boiling
        features down to compact but powerful abstractions, and controlling feature bloat carefully.

  - type: textarea

        // In practice, this would be more sophisticated
        int uniqueBytes = countUniqueBytes(data);
        double entropy = (double) uniqueBytes / 256.0;

        // Estimate compression ratio based on entropy
        // Lower entropy = better compression
        return 0.3 + (entropy * 0.6); // Range from 30% to 90%
    }

    @Override
    public int getMinCompressionSize() {

                || height < getThumbnailHtmlImageMinHeightAsInteger().intValue()) {
            return false;
        }

        final float ratio = getThumbnailHtmlImageMaxAspectRatioAsDecimal().floatValue();
        if ((float) width / (float) height > ratio || (float) height / (float) width > ratio) {
            return false;
        }

        return true;
    }

    String getHttpProxyHost();

        if (width < TABLET_WIDTH) {
            float ratio = (float) width / (float) TABLET_WIDTH;
            if (ratio < 0.5) {
                ratio = 0.5f;
            }
            highlightInfo.fragmentSize((int) (highlightInfo.getFragmentSize() * ratio));
        }
    }

    /**
     * Gets the URL link for a document with proper protocol handling.

      // Since scale is a positive int, index is in [0, scale], and (dataset.length - 1) is a
      // non-negative int, we can do long-arithmetic on index * (dataset.length - 1) / scale to get
      // a rounded ratio and a remainder which can be expressed as ints, without risk of overflow:
      int quotient = (int) LongMath.divide(numerator, scale, RoundingMode.DOWN);
      int remainder = (int) (numerator - (long) quotient * scale);