⚡️ Speed up function fibonacci by 8%

[codeflash-ai]

📄 8% (0.08x) speedup for fibonacci in code_to_optimize_js_esm/fibonacci.js

⏱️ Runtime : 90.9 microseconds → 84.2 microseconds (best of 43 runs)

📝 Explanation and details

Runtime improvement: the optimized version reduces wall-clock time from 90.9 μs to 84.2 μs (~7% faster) by lowering per-iteration loop overhead in the hot path that extends the module-level Fibonacci cache.

What changed (concrete):

• Replaced the for-loop (for (let i = len; i <= n; ++i) { arr[i] = c; ... }) with a while loop and a single post-increment store (let i = len; while (i <= n) { arr[i++] = c; ... }).
• Kept the important micro-optimizations from the original (local reference to the module array, local variables a and b for the two previous Fibonacci values).

Why this speeds up the code:

• Fewer operations per iteration: using arr[i++] = c combines the array store and index increment into one expression instead of doing two separate steps (arr[i] = c; ++i). That removes one increment/assignment bytecode per iteration.
• Simpler loop shape: moving the index update inside the body (while + post-increment) eliminates the separate loop-update phase and yields a tighter, more predictable loop that JITs into simpler machine code.
• Better JIT/IC behavior: the tighter, monomorphic loop body (same local variables and same kinds of operations each iteration) helps engines like V8 produce faster optimized code and fewer deoptimizations.
• These savings multiply with n: the more iterations required to extend the cache, the larger the absolute gain.

Impact on workloads and tests:

• Biggest wins happen when the function must extend the cache (moderate-to-large n) or is called repeatedly in tight loops — exactly the hot paths exercised by the performance tests (e.g., fibonacci(78), fibonacci(500), fibonacci(1000)). The annotated tests show measurable per-test improvements (small-n micro-tests and cached lookups are slightly faster as well).
• Cached lookups (n < cached length) remain O(1) and are unaffected functionally; the optimization only reduces the cost of populating the cache.
• There is no behavioral change: same results for all tests, and no new dependencies or corner-case regressions were introduced.

Trade-offs:

• This is a pure micro-optimization focused on runtime; it does not change algorithmic complexity or memory usage. The runtime benefit (7% measured) was the acceptance criterion and is the primary positive here.

In short: by simplifying the loop and reducing per-iteration work (combining the store + increment and removing the loop-update phase), the optimized function produces a small but reliable runtime win, especially valuable in hot paths that build the Fibonacci cache repeatedly.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 13 Passed
🌀 Generated Regression Tests	✅ 35 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
fibonacci.test.js::fibonacci returns 0 for n=0	22.0μs	20.0μs	10.2%✅
fibonacci.test.js::fibonacci returns 1 for n=1	875ns	750ns	16.7%✅
fibonacci.test.js::fibonacci returns 1 for n=2	3.33μs	2.96μs	12.7%✅
fibonacci.test.js::fibonacci returns 233 for n=13	1.58μs	1.54μs	2.79%✅
fibonacci.test.js::fibonacci returns 5 for n=5	2.00μs	1.83μs	9.11%✅
fibonacci.test.js::fibonacci returns 55 for n=10	1.71μs	1.58μs	7.90%✅

🌀 Click to see Generated Regression Tests

// imports
import { fibonacci } from '../fibonacci.js';

// unit tests
describe('fibonacci', () => {
    // Basic Test Cases
    describe('Basic functionality', () => {
        test('should handle normal input (first 11 Fibonacci numbers)', () => {
            // Verify the basic known Fibonacci sequence for n = 0..10
            const expected = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55];
            expected.forEach((val, n) => {
                // each index n should produce expected[n]
                expect(fibonacci(n)).toBe(val);
            });
        });

        test('should be consistent across repeated calls and produce correct next value', () => {
            // Call for 10 to ensure internal memo (module-scoped cache) is populated,
            // then request 11 which should use the cached values or extend them correctly.
            const f10 = fibonacci(10); // expected 55
            expect(f10).toBe(55);  // 1.67μs -> 1.54μs (8.11% faster)

            const f11 = fibonacci(11); // expected 89
            expect(f11).toBe(89);

            // Calling again doesn't change result
            expect(fibonacci(10)).toBe(55);
            expect(fibonacci(11)).toBe(89);
        });

        test('should accept numeric strings that coerce to numbers (e.g., "8")', () => {
            // The implementation will coerce the string when doing arithmetic in recursion,
            // so "8" should behave like 8 and return 21.
            expect(fibonacci('8')).toBe(21);
        });
    });

    // Edge Test Cases
    describe('Edge cases', () => {
        test('should return input for negative integers due to early n <= 1 guard', () => {
            // Note: the implementation has `if (n <= 1) return n;`
            // so for negative integers it simply returns the negative number.
            expect(fibonacci(-1)).toBe(-1);  // 750ns -> 626ns (19.8% faster)
            expect(fibonacci(-5)).toBe(-5);
        });

        test('should return null when passed null (null <= 1 is true and it returns n)', () => {
            // null coerces to 0 in numeric comparisons; the function returns n directly.
            expect(fibonacci(null)).toBe(null);
        });

        test('should handle non-integer numbers via the recursive fallback (example: 1.5)', () => {
            // For 1.5:
            // - Not an integer, so the function takes the recursive fallback branch:
            //   fibonacci(0.5) + fibonacci(-0.5) -> 0.5 + (-0.5) === 0
            expect(fibonacci(1.5)).toBeCloseTo(0, 12);

            // Another non-integer example: 2.5 -> fibonacci(1.5) + fibonacci(0.5) -> 0 + 0.5 = 0.5
            expect(fibonacci(2.5)).toBeCloseTo(0.5, 12);
        });

        test('should work for moderate integer values near the safe integer limit (sanity check)', () => {
            // Ensure it computes a value for a moderately large n without blowing up.
            // We pick n = 30 which is well within safe integer range and should be deterministic.
            expect(fibonacci(30)).toBe(832040);
        });
    });

    // Large Scale Test Cases
    describe('Performance tests', () => {
        test('should handle large inputs efficiently (compute fibonacci(78) within a reasonable time)', () => {
            // Choose n = 78: it's large, but within Number safe integer range
            // and under the test constraint of not doing loops > 1000 iterations.
            const n = 78;
            const expected78 = 8944394323791464; // known Fibonacci(78)

            const start = Date.now();
            const value1 = fibonacci(n);
            const time1 = Date.now() - start;

            expect(value1).toBe(expected78);

            // Second call should be faster (uses cached array extension performed during first call)
            const start2 = Date.now();
            const value2 = fibonacci(n);
            const time2 = Date.now() - start2;

            expect(value2).toBe(expected78);

            // Basic performance expectations:
            // - First computation should complete (we allow a generous threshold to avoid flaky failures).
            // - Second computation (cached) should be quick.
            // These thresholds are intentionally relaxed to be CI friendly.
            expect(time1).toBeLessThan(500); // first call should be < 500ms
            expect(time2).toBeLessThan(50);  // cached second call should be < 50ms
        });

        test('should compute sequentially larger fibonacci values efficiently by leveraging cache', () => {
            // Compute a value, then compute the next one which should be derivable quickly.
            const a = fibonacci(70); // compute F70
            const b = fibonacci(71); // F71 = F70 + F69
            // Basic sanity checks for monotonic growth for n>=2
            expect(typeof a).toBe('number');
            expect(typeof b).toBe('number');
            expect(b).toBeGreaterThanOrEqual(a);
            // Known relation: F71 = F70 + F69, so b - a === F69 (positive)
            // We won't assert exact F69 here to avoid duplicating large constants, but ensure non-zero increment.
            expect(b - a).toBeGreaterThan(0);
        });
    });
});

// imports
import { fibonacci } from '../fibonacci.js';

// unit tests
describe('fibonacci', () => {
    // Basic Test Cases
    describe('Basic functionality', () => {
        test('should handle normal input', () => {
            // Basic known values from the Fibonacci sequence
            expect(fibonacci(0)).toBe(0);   // F0
            expect(fibonacci(1)).toBe(1);   // F1
            expect(fibonacci(2)).toBe(1);   // F2
            expect(fibonacci(3)).toBe(2);   // F3
            expect(fibonacci(4)).toBe(3);   // F4
            expect(fibonacci(5)).toBe(5);   // F5
            expect(fibonacci(10)).toBe(55); // F10
            // Also verify a mid-range value to ensure iterative buildup is correct
            expect(fibonacci(20)).toBe(6765); // F20
        });
    });

    // Edge Test Cases
    describe('Edge cases', () => {
        test('should return the input for negative numbers (handles n <= 1 early return)', () => {
            // The implementation returns n directly when n <= 1.
            // For negative integers this means the function echoes the input.
            expect(fibonacci(-1)).toBe(-1);
            expect(fibonacci(-5)).toBe(-5);
        });

        test('should handle non-integer numeric inputs via recursive fallback', () => {
            // For non-integer numbers the implementation uses the recursive fallback.
            // Example: fibonacci(1.5) -> fibonacci(0.5) + fibonacci(-0.5) => 0.5 + (-0.5) = 0
            expect(fibonacci(1.5)).toBeCloseTo(0); // exact 0 in this implementation
            // A fractional <= 1 should return itself (base case)
            expect(fibonacci(0.5)).toBeCloseTo(0.5);
        });

        test('should coerce numeric strings through arithmetic path and compute correctly', () => {
            // Passing a numeric string goes to the recursive arithmetic path:
            // fibonacci('7') -> fibonacci(6) + fibonacci(5) -> 8 + 5 = 13
            expect(fibonacci('7')).toBe(13);  // 3.21μs -> 2.54μs (26.2% faster)
        });

        test('should be deterministic and idempotent across repeated calls', () => {
            // Repeated calls with same input must return the same result.
            const a = fibonacci(15);
            const b = fibonacci(15);
            expect(a).toBe(610);
            expect(b).toBe(610);
            expect(a).toBe(b);
        });
    });

    // Large Scale Test Cases
    describe('Performance tests', () => {
        test('should handle large inputs efficiently and produce correct magnitude (n = 1000)', () => {
            // This verifies scalability:
            // - The function should return a finite number for n = 1000 (no overflow to Infinity).
            // - The order of magnitude (number of digits) is checked using log10 approximation.
            // Note: we avoid asserting exact integer equality because JS Number cannot represent
            // all digits of very large Fibonacci numbers precisely; instead we assert magnitude.
            const start = Date.now();
            const result = fibonacci(1000);
            const duration = Date.now() - start;

            // Result should be a finite number (not NaN or Infinity)
            expect(Number.isFinite(result)).toBe(true);

            // Number of decimal digits for F(1000) is known to be 209 (approx).
            // Use Math.log10 on absolute value to estimate digit count.
            const digits = Math.floor(Math.log10(Math.abs(result))) + 1;
            expect(digits).toBe(209);

            // Ensure the computation completes within a reasonable time (generous threshold).
            // This is to detect pathological exponential recursion on large inputs.
            // Threshold is lenient (2 seconds) to avoid CI flakiness on slower machines.
            expect(duration).toBeLessThan(2000);
        });

        test('should reuse cached computations so subsequent smaller queries are fast', () => {
            // After computing a large n, subsequent requests for smaller n should be instantaneous
            // because the module caches intermediate Fibonacci values in its internal array.
            // We first ensure a large value is computed (this will populate cache up to that n).
            fibonacci(500); // populate cache up to 500

            // Now measure time to fetch a much smaller value that should already be cached.
            const t0 = Date.now();
            const small = fibonacci(30); // small lookup, should be O(1)
            const t1 = Date.now();

            expect(small).toBe(832040); // F30
            // Expect that the lookup itself is very fast; allow a small margin (50ms).
            expect(t1 - t0).toBeLessThan(50);
        });
    });
});

To edit these changes git checkout codeflash/optimize-fibonacci-mkxgxy1h and push.