From 9b756436612f21e0d67d95b1c80715a033ef8831 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Fri, 30 Jan 2026 23:05:32 +0000
Subject: [PATCH] Optimize sorter
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## Optimization Summary

The optimized code achieves a **3,206x speedup** (from 3.28 seconds to 1.02 milliseconds) by replacing the O(n²) bubble sort algorithm with Python's built-in Timsort via `arr.sort()`, which runs in O(n log n) time.

## Key Changes

**Algorithmic replacement:**
- **Original**: Nested loops iterating ~61 million times for moderate-sized lists, with element-by-element swaps
- **Optimized**: Single `arr.sort()` call leveraging Python's highly-optimized C implementation of Timsort

**Fallback handling:**
- Added try/except to handle edge cases where `arr` might not support `.sort()` (e.g., custom sequence types)
- Fallback creates a temporary sorted list and writes values back element-by-element to preserve in-place mutation semantics

## Why This Is Faster

1. **Complexity reduction**: O(n²) → O(n log n) means dramatically fewer operations as list size grows
2. **Native optimization**: Python's `list.sort()` is implemented in highly-optimized C code, versus interpreted Python loops
3. **Memory efficiency**: Timsort is an in-place algorithm with minimal overhead, while the original performs redundant comparisons and swaps

The line profiler shows the original spent 42.4 seconds across 61.7 million loop iterations, while the optimized version completes in 0.0017 seconds with just the `arr.sort()` call.

## Test Results Analysis

All test cases show significant speedups:
- **Small lists** (2-10 elements): 20-197% faster (microseconds → microseconds)
- **Medium lists** (100 elements): 3,017-5,586% faster
- **Large lists** (500-1000 elements): **14,174-122,233% faster**

The optimization particularly excels with larger datasets—exactly where the O(n²) vs O(n log n) difference matters most. The `test_full_bubble_coverage.py` reference shows this function is tested with 5,000-element lists, meaning production usage would see massive benefits.

## Impact on Existing Workloads

Based on `function_references`, the `sorter` function is called from:
1. **`sort_from_another_file`**: Direct pass-through will see full speedup benefits
2. **`test_full_bubble_coverage.py`**: Tests with 5,000-element lists—these will complete **orders of magnitude faster**
3. **`compute_and_sort`**: Calls `sorter(arr.copy())` as part of data processing pipeline—sorting overhead becomes negligible

All references preserve the in-place mutation contract and return value, so the optimization is a drop-in replacement with no behavioral changes except dramatically reduced execution time.
---
 code_to_optimize/bubble_sort.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/code_to_optimize/bubble_sort.py b/code_to_optimize/bubble_sort.py
index 9e97f63a0..b2309395b 100644
--- a/code_to_optimize/bubble_sort.py
+++ b/code_to_optimize/bubble_sort.py
@@ -1,10 +1,14 @@
 def sorter(arr):
     print("codeflash stdout: Sorting list")
-    for i in range(len(arr)):
-        for j in range(len(arr) - 1):
-            if arr[j] > arr[j + 1]:
-                temp = arr[j]
-                arr[j] = arr[j + 1]
-                arr[j + 1] = temp
+    # Use the object's in-place sort if available (fast, O(n log n)).
+    # If arr does not provide sort(), fall back to sorting a temporary list
+    # and writing values back element-by-element to preserve in-place mutation.
+    try:
+        arr.sort()
+    except (AttributeError, TypeError):
+        tmp = list(arr)
+        tmp.sort()
+        for i in range(len(tmp)):
+            arr[i] = tmp[i]
     print(f"result: {arr}")
     return arr