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⚡️ Speed up method CodeCommitProvider._get_language_percentages by 68%#47

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⚡️ Speed up method CodeCommitProvider._get_language_percentages by 68%#47
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@codeflash-ai codeflash-ai bot commented Oct 20, 2025

📄 68% (0.68x) speedup for CodeCommitProvider._get_language_percentages in pr_agent/git_providers/codecommit_provider.py

⏱️ Runtime : 8.98 microseconds 5.35 microseconds (best of 80 runs)

📝 Explanation and details

The optimization achieves a 67% speedup by replacing Python's Counter class with manual dictionary accumulation and pre-computing the division factor.

Key optimizations applied:

  1. Eliminated Counter overhead: Replaced Counter(extensions) with a simple dictionary and manual counting using counts.get(ext, 0) + 1. This avoids the overhead of Counter's constructor and internal optimizations that aren't beneficial for small datasets.

  2. Hoisted division operation: Pre-computed inv_total = 100 / total_files once instead of performing division for each language in the dict comprehension. This transforms count / total_files * 100 into count * inv_total, reducing repeated division operations.

  3. Separated dict construction: Replaced the dict comprehension with explicit loops, which eliminates the overhead of comprehension setup and allows for more predictable memory access patterns.

Why this works:

  • Counter is optimized for large datasets and complex counting scenarios, but introduces unnecessary overhead for simple extension counting
  • Division is more expensive than multiplication in Python, so pre-computing the inverse factor provides measurable gains
  • The explicit loop approach has better cache locality and fewer function call overheads compared to the comprehension + Counter combination

Performance characteristics:
The line profiler shows the Counter operation took 61.2% of the original runtime (22,760ns), while the manual counting approach distributes the work more efficiently across multiple lighter operations. This optimization is particularly effective for small to medium-sized file lists (typical PR scenarios) where Counter's optimizations don't justify its setup costs.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 13 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Existing Unit Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
unittest/test_codecommit_provider.py::TestCodeCommitProvider.test_get_language_percentages 8.98μs 5.35μs 67.9%✅

To edit these changes git checkout codeflash/optimize-CodeCommitProvider._get_language_percentages-mgzjfe4q and push.

Codeflash

The optimization achieves a 67% speedup by replacing Python's `Counter` class with manual dictionary accumulation and pre-computing the division factor.

**Key optimizations applied:**

1. **Eliminated Counter overhead**: Replaced `Counter(extensions)` with a simple dictionary and manual counting using `counts.get(ext, 0) + 1`. This avoids the overhead of Counter's constructor and internal optimizations that aren't beneficial for small datasets.

2. **Hoisted division operation**: Pre-computed `inv_total = 100 / total_files` once instead of performing division for each language in the dict comprehension. This transforms `count / total_files * 100` into `count * inv_total`, reducing repeated division operations.

3. **Separated dict construction**: Replaced the dict comprehension with explicit loops, which eliminates the overhead of comprehension setup and allows for more predictable memory access patterns.

**Why this works:**
- Counter is optimized for large datasets and complex counting scenarios, but introduces unnecessary overhead for simple extension counting
- Division is more expensive than multiplication in Python, so pre-computing the inverse factor provides measurable gains
- The explicit loop approach has better cache locality and fewer function call overheads compared to the comprehension + Counter combination

**Performance characteristics:**
The line profiler shows the Counter operation took 61.2% of the original runtime (22,760ns), while the manual counting approach distributes the work more efficiently across multiple lighter operations. This optimization is particularly effective for small to medium-sized file lists (typical PR scenarios) where Counter's optimizations don't justify its setup costs.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 20, 2025 19:38
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 20, 2025
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