⚡️ Speed up method Algorithms.fibonacci by 12%

[codeflash-ai]

📄 12% (0.12x) speedup for Algorithms.fibonacci in code_to_optimize/java/src/main/java/com/example/Algorithms.java

⏱️ Runtime : 19.6 milliseconds → 17.6 milliseconds (best of 8 runs)

📝 Explanation and details

Runtime improvement (primary): The optimized implementation reduces wall-clock time from 19.6 ms to 17.6 ms — an ~11% speedup — by lowering the per-iteration instruction cost in the hot loop of the O(log n) fast-doubling algorithm.

What changed (specific optimization):

• Replaced the bit test ((n >> i) & 1) with an explicit mask computed as (1 << i) and tested via (n & mask) == 0.
• Everything else (algorithm, loop bounds, early return for n <= 1, arithmetic for c and d) is unchanged.

Why this speeds things up:

• The loop is executed O(log n) times and is the hot path tested by your performance tests. The original expression (n >> i) creates a variable-width right shift of n and then masks the low bit; that produces a dependency on n and a variable-shift operation each iteration.
• The new approach computes a single left shift of a small constant (1 << i) to form mask, then performs a single AND with n. On many runtimes (JVM/JIT), shifting a constant and doing one AND is fewer CPU micro-ops and has simpler data dependencies than a variable-width right-shift of a potentially larger value. This lowers the constant factor per iteration.
• Fewer dependent operations improves instruction throughput and branch prediction in the hot loop, giving the observed ~11% runtime reduction without changing Big-O.

Behavioral/compatibility notes:

• The function semantics are preserved for all inputs (including handling for n <= 1). The change only alters how the chosen bit is checked, not which bit is checked.
• No other metrics (memory usage, algorithmic complexity) regress; this is a pure constant-factor improvement.

Impact on workloads and tests:

• This optimization matters most when fibonacci() is called with large n (many bits set to examine). Your annotated tests include calls with Integer.MAX_VALUE and timeouts for large inputs — exactly the workload that benefits because the hot loop runs more iterations and the per-iteration savings accumulate.
• The unit/regression tests provided confirm correctness for base cases, boundary values (F(92)), overflow behavior (F(93)), random samples, and repeated calls; all remain valid and deterministic.

When to expect gains:

• Best for workloads that call fibonacci many times or with large n (deep bit-length). For tiny n the absolute wall-clock improvement will be small, but for large/iterated workloads the ~11% constant-factor win is meaningful.

Summary:

• Primary benefit: 11% runtime reduction.
• How: simplified the bit test used inside the fast-doubling loop (replace variable right-shift + mask with an explicit mask + AND), reducing per-iteration CPU work and dependencies.
• Safe: behavior-preserving and validated by the provided tests, and particularly effective in the hot-path scenarios you exercise in your performance tests.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 16 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	Coverage data not available

🌀 Click to see Generated Regression Tests

package com.example;

import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;

import java.math.BigInteger;
import java.time.Duration;
import java.util.Random;

import static org.junit.jupiter.api.Assertions.*;
import com.example.Algorithms;

public class AlgorithmsTest {

    private Algorithms instance;

    @BeforeEach
    void setUp() {
        instance = new Algorithms();
    }

    @Test
    void testBaseCase_Zero_ReturnsZero() {
        assertEquals(0L, instance.fibonacci(0));
    }

    @Test
    void testBaseCase_One_ReturnsOne() {
        assertEquals(1L, instance.fibonacci(1));
    }

    @Test
    void testNegativeInput_ReturnsSameNegativeValue() {
        // Implementation returns n for any n <= 1, so negative values are returned unchanged.
        assertEquals(-5L, instance.fibonacci(-5));
    }

    @Test
    void testTypicalInputs_SmallSequence_ReturnsExpectedValues() {
        // Known Fibonacci sequence values for n = 0..10
        long[] expected = {
                0L, 1L, 1L, 2L, 3L, 5L, 8L, 13L, 21L, 34L, 55L
        };
        for (int i = 0; i < expected.length; i++) {
            assertEquals(expected[i], instance.fibonacci(i), "F(" + i + ") should match expected value");
        }
    }

    @Test
    void testBoundary_F92_ReturnsExpectedValue() {
        // F(92) is the largest Fibonacci number that fits in a signed 64-bit long without overflow
        long expectedF92 = 7540113804746346429L;
        assertEquals(expectedF92, instance.fibonacci(92));
    }

    @Test
    void testOverflow_F93_MatchesBigIntegerLongValue() {
        // For n >= 93, the Fibonacci number exceeds Long.MAX_VALUE and will wrap.
        // Compute Fibonacci(93) using BigInteger and compare the longValue() (which simulates Java long overflow behavior).
        int n = 93;
        BigInteger a = BigInteger.ZERO;
        BigInteger b = BigInteger.ONE;
        for (int i = 1; i <= n; i++) {
            BigInteger next = a.add(b);
            a = b;
            b = next;
        }
        // 'a' now holds F(n)
        long expectedWrapped = a.longValue(); // lower 64 bits as signed long
        assertEquals(expectedWrapped, instance.fibonacci(n));
    }

    @Test
    void testConsistency_WithNaiveImplementation_ForRangeUpTo92() {
        // Compare the fast doubling implementation against a simple iterative implementation for 0..92
        // (92 is safe from overflow in a signed long)
        for (int n = 0; n <= 92; n++) {
            long naive = naiveFibonacciLong(n);
            long fast = instance.fibonacci(n);
            assertEquals(naive, fast, "Mismatch at n=" + n);
        }
    }

    @Test
    void testLargeInput_Performance_CompletesQuicklyAndDeterministic() {
        // Ensure that even a very large int input completes quickly (method is O(log n) time).
        // Also check determinism (same input returns same output on repeated calls).
        int largeInput = Integer.MAX_VALUE;
        assertTimeout(Duration.ofMillis(200), () -> {
            long v1 = instance.fibonacci(largeInput);
            long v2 = instance.fibonacci(largeInput);
            assertEquals(v1, v2, "Consecutive calls with the same large input should produce identical results");
        });
    }

    @Test
    void testRandomSampleConsistency_ForValuesWithinSafeRange() {
        // Randomly sample some n values <= 92 and compare with naive implementation.
        Random rnd = new Random(12345);
        for (int i = 0; i < 20; i++) {
            int n = rnd.nextInt(93); // 0..92 inclusive
            long expected = naiveFibonacciLong(n);
            assertEquals(expected, instance.fibonacci(n), "Random sample mismatch at n=" + n);
        }
    }

    @Test
    void testIdempotence_RepeatedCallsReturnSameValue() {
        int n = 50;
        long first = instance.fibonacci(n);
        long second = instance.fibonacci(n);
        long third = instance.fibonacci(n);
        assertEquals(first, second);
        assertEquals(second, third);
    }

    // Helper: naive iterative fibonacci returning long (suitable for n <= 92)
    private long naiveFibonacciLong(int n) {
        if (n <= 1) return n;
        long a = 0L;
        long b = 1L;
        for (int i = 2; i <= n; i++) {
            long next = a + b;
            a = b;
            b = next;
        }
        return b;
    }
}

package com.example;

import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;

import java.time.Duration;

import static org.junit.jupiter.api.Assertions.*;

import com.example.Algorithms;

public class AlgorithmsTest {

    private Algorithms instance;

    @BeforeEach
    void setUp() {
        instance = new Algorithms();
    }

    @Test
    void testZero_ReturnsZero() {
        assertEquals(0L, instance.fibonacci(0), "F(0) should be 0");
    }

    @Test
    void testOne_ReturnsOne() {
        assertEquals(1L, instance.fibonacci(1), "F(1) should be 1");
    }

    @Test
    void testTwo_ReturnsOne() {
        assertEquals(1L, instance.fibonacci(2), "F(2) should be 1");
    }

    @Test
    void testThree_ReturnsTwo() {
        assertEquals(2L, instance.fibonacci(3), "F(3) should be 2");
    }

    @Test
    void testTen_ReturnsFiftyFive() {
        assertEquals(55L, instance.fibonacci(10), "F(10) should be 55");
    }

    @Test
    void testSequenceProperty_HoldsForN20() {
        long f18 = instance.fibonacci(18);
        long f19 = instance.fibonacci(19);
        long f20 = instance.fibonacci(20);
        // For non-overflowing indices, F(n) = F(n-1) + F(n-2)
        assertEquals(f19 + f18, f20, "F(20) should equal F(19) + F(18)");
    }

    @Test
    void testNegativeInput_ReturnsSameNegativeValue() {
        // According to implementation, for n <= 1 the method returns n,
        // so negative inputs are returned as-is (no exception).
        assertEquals(-5L, instance.fibonacci(-5), "Negative input should be returned as-is");
    }

    @Test
    void testMaxNonOverflowBoundary_92_ReturnsExpected() {
        // F(92) is the largest Fibonacci number that fits in signed 64-bit long
        long expectedF92 = 7540113804746346429L;
        assertEquals(expectedF92, instance.fibonacci(92), "F(92) should match the known value");
    }

    @Test
    void testOverflowAt93_ReturnsWrappedLongValue() {
        // F(93) overflows signed 64-bit and wraps; check the wrapped result
        long expectedWrappedF93 = -624658365858767488L;
        assertEquals(expectedWrappedF93, instance.fibonacci(93), "F(93) should produce the wrapped long value");
    }

    @Test
    void testMultipleCalls_ConsistentResults() {
        long first = instance.fibonacci(50);
        long second = instance.fibonacci(50);
        assertEquals(first, second, "Multiple calls with same input should yield the same result");
    }

    @Test
    void testPerformance_LargeN_CompletesQuickly() {
        // Algorithm is O(log n). Ensure it completes quickly even for large n.
        assertTimeout(Duration.ofSeconds(1), () -> {
            // We only assert it completes within the timeout; the returned value is not asserted here.
            instance.fibonacci(1_000_000);
        });
    }

    @Test
    void testIntegerMaxValue_CompletesQuickly() {
        // Ensure it handles large int inputs (bit-wise) quickly
        assertTimeout(Duration.ofSeconds(1), () -> {
            instance.fibonacci(Integer.MAX_VALUE);
        });
    }
}

To edit these changes git checkout codeflash/optimize-Algorithms.fibonacci-ml18jcbx and push.