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11 changes: 11 additions & 0 deletions codeflash/result/common_tags.py
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from __future__ import annotations


def find_common_tags(articles: list[dict[str, list[str]]]) -> set[str]:
if not articles:
return set()

common_tags = articles[0].get("tags", [])
for article in articles[1:]:
common_tags = [tag for tag in common_tags if tag in article.get("tags", [])]
return set(common_tags)
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@codeflash-ai codeflash-ai bot Jan 28, 2026

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⚡️Codeflash found 7,931% (79.31x) speedup for find_common_tags in codeflash/result/common_tags.py

⏱️ Runtime : 580 milliseconds → 7.22 milliseconds (best of 43 runs)

📝 Explanation and details

The optimized code achieves a 79x speedup (7931% faster) by replacing list-based filtering with set-based intersection operations.

Key Changes:

  1. Initial conversion to set: common_tags = set(articles[0].get("tags", [])) immediately creates a set instead of keeping a list
  2. In-place set intersection: common_tags.intersection_update(article.get("tags", [])) replaces the list comprehension

Why This Is Faster:

The original implementation uses [tag for tag in common_tags if tag in article.get("tags", [])], which has O(n*m) complexity for each iteration—every tag in common_tags must be checked against every tag in the current article's tag list using in on a list (linear search).

With sets, intersection_update() leverages hash-based lookups with O(1) average time complexity per element, reducing each iteration to approximately O(min(n,m)) where n and m are the sizes of the two sets being intersected.

Performance Impact:

Line profiler shows the critical loop execution dropped from 638ms (99.6% of runtime) to 11.8ms (80.5% of runtime)—a 54x improvement on the bottleneck line alone.

The optimization is particularly effective for:

  • Large tag lists: test_large_number_of_tags shows 5618% speedup (4.54ms → 79.3μs)
  • Many articles: test_large_scale_test_cases shows 11,000%+ speedup (382ms → 3.44ms)
  • All test cases benefit: Even small inputs see 10-50% improvements due to more efficient set operations

The dramatic speedup occurs because the algorithm now avoids quadratic behavior when checking tag membership, making it scale much better with both the number of articles and the size of tag lists.

✅ Correctness verification report:

Test Status
⚙️ Existing Unit Tests ✅ 2 Passed
🌀 Generated Regression Tests ✅ 29 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests ✅ 2 Passed
📊 Tests Coverage 100.0%
⚙️ Click to see Existing Unit Tests
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_common_tags.py::test_common_tags_1 6.04μs 4.39μs 37.7%✅
🌀 Click to see Generated Regression Tests 
# imports
# function to test
from __future__ import annotations

from codeflash.result.common_tags import find_common_tags

# unit tests


def test_single_article():
    # Single article should return its tags
    articles = [{"tags": ["python", "coding", "tutorial"]}]
    codeflash_output = find_common_tags(articles)  # 2.13μs -> 1.46μs (45.9% faster)
    # Outputs were verified to be equal to the original implementation


def test_multiple_articles_with_common_tags():
    # Multiple articles with common tags should return the common tags
    articles = [{"tags": ["python", "coding"]}, {"tags": ["python", "data"]}, {"tags": ["python", "machine learning"]}]
    codeflash_output = find_common_tags(articles)  # 3.44μs -> 2.31μs (48.5% faster)
    # Outputs were verified to be equal to the original implementation


def test_empty_list_of_articles():
    # Empty list of articles should return an empty set
    articles = []
    codeflash_output = find_common_tags(articles)  # 872ns -> 491ns (77.6% faster)
    # Outputs were verified to be equal to the original implementation


def test_articles_with_no_common_tags():
    # Articles with no common tags should return an empty set
    articles = [{"tags": ["python"]}, {"tags": ["java"]}, {"tags": ["c++"]}]
    codeflash_output = find_common_tags(articles)  # 2.46μs -> 2.16μs (13.9% faster)
    # Outputs were verified to be equal to the original implementation


def test_articles_with_empty_tag_lists():
    # Articles with some empty tag lists should return an empty set
    articles = [{"tags": []}, {"tags": ["python"]}, {"tags": ["python", "java"]}]
    codeflash_output = find_common_tags(articles)  # 2.01μs -> 1.94μs (3.55% faster)
    # Outputs were verified to be equal to the original implementation


def test_all_articles_with_empty_tag_lists():
    # All articles with empty tag lists should return an empty set
    articles = [{"tags": []}, {"tags": []}, {"tags": []}]
    codeflash_output = find_common_tags(articles)  # 2.07μs -> 1.78μs (16.3% faster)
    # Outputs were verified to be equal to the original implementation


def test_tags_with_special_characters():
    # Tags with special characters should be handled correctly
    articles = [{"tags": ["python!", "coding"]}, {"tags": ["python!", "data"]}]
    codeflash_output = find_common_tags(articles)  # 2.49μs -> 1.90μs (31.0% faster)
    # Outputs were verified to be equal to the original implementation


def test_case_sensitivity():
    # Tags with different cases should not be considered the same
    articles = [{"tags": ["Python", "coding"]}, {"tags": ["python", "data"]}]
    codeflash_output = find_common_tags(articles)  # 2.19μs -> 1.77μs (23.7% faster)
    # Outputs were verified to be equal to the original implementation


def test_large_number_of_articles():
    # Large number of articles with a common tag should return that tag
    articles = [{"tags": ["common_tag", f"tag{i}"]} for i in range(1000)]
    codeflash_output = find_common_tags(articles)  # 223μs -> 149μs (49.2% faster)
    # Outputs were verified to be equal to the original implementation


def test_large_number_of_tags():
    # Large number of tags with some common tags should return the common tags
    articles = [{"tags": [f"tag{i}" for i in range(1000)]}, {"tags": [f"tag{i}" for i in range(500, 1500)]}]
    expected = {f"tag{i}" for i in range(500, 1000)}
    codeflash_output = find_common_tags(articles)  # 4.54ms -> 79.3μs (5618% faster)
    # Outputs were verified to be equal to the original implementation


def test_mixed_length_of_tag_lists():
    # Articles with mixed length of tag lists should return the common tags
    articles = [{"tags": ["python", "coding"]}, {"tags": ["python"]}, {"tags": ["python", "coding", "tutorial"]}]
    codeflash_output = find_common_tags(articles)  # 2.71μs -> 2.19μs (23.3% faster)
    # Outputs were verified to be equal to the original implementation


def test_tags_with_different_data_types():
    # Tags with different data types should only consider strings
    articles = [{"tags": ["python", 123]}, {"tags": ["python", "123"]}]
    codeflash_output = find_common_tags(articles)  # 2.36μs -> 1.96μs (20.4% faster)
    # Outputs were verified to be equal to the original implementation


def test_performance_with_large_data():
    # Performance with large data should return the common tag
    articles = [{"tags": ["common_tag", f"tag{i}"]} for i in range(10000)]
    codeflash_output = find_common_tags(articles)  # 2.24ms -> 1.48ms (51.2% faster)
    # Outputs were verified to be equal to the original implementation


def test_scalability_with_increasing_tags():
    # Scalability with increasing tags should return the common tag
    articles = [{"tags": ["common_tag"] + [f"tag{i}" for i in range(j)]} for j in range(1, 1001)]
    codeflash_output = find_common_tags(articles)  # 471μs -> 323μs (45.5% faster)
    # Outputs were verified to be equal to the original implementation
# imports
# function to test
from __future__ import annotations

from codeflash.result.common_tags import find_common_tags

# unit tests


def test_empty_input_list():
    # Test with an empty list
    codeflash_output = find_common_tags([])  # 651ns -> 471ns (38.2% faster)
    # Outputs were verified to be equal to the original implementation


def test_single_article():
    # Test with a single article with tags
    codeflash_output = find_common_tags(
        [{"tags": ["python", "coding", "development"]}]
    )  # 1.73μs -> 1.45μs (19.3% faster)
    # Test with a single article with no tags
    codeflash_output = find_common_tags([{"tags": []}])  # 611ns -> 521ns (17.3% faster)
    # Outputs were verified to be equal to the original implementation


def test_multiple_articles_some_common_tags():
    # Test with multiple articles having some common tags
    articles = [
        {"tags": ["python", "coding", "development"]},
        {"tags": ["python", "development", "tutorial"]},
        {"tags": ["python", "development", "guide"]},
    ]
    codeflash_output = find_common_tags(articles)  # 3.15μs -> 2.41μs (30.3% faster)

    articles = [{"tags": ["tech", "news"]}, {"tags": ["tech", "gadgets"]}, {"tags": ["tech", "reviews"]}]
    codeflash_output = find_common_tags(articles)  # 1.67μs -> 1.15μs (45.2% faster)
    # Outputs were verified to be equal to the original implementation


def test_multiple_articles_no_common_tags():
    # Test with multiple articles having no common tags
    articles = [{"tags": ["python", "coding"]}, {"tags": ["development", "tutorial"]}, {"tags": ["guide", "learning"]}]
    codeflash_output = find_common_tags(articles)  # 2.41μs -> 2.20μs (9.53% faster)

    articles = [{"tags": ["apple", "banana"]}, {"tags": ["orange", "grape"]}, {"tags": ["melon", "kiwi"]}]
    codeflash_output = find_common_tags(articles)  # 1.28μs -> 1.05μs (21.7% faster)
    # Outputs were verified to be equal to the original implementation


def test_articles_with_duplicate_tags():
    # Test with articles having duplicate tags
    articles = [
        {"tags": ["python", "python", "coding"]},
        {"tags": ["python", "development", "python"]},
        {"tags": ["python", "guide", "python"]},
    ]
    codeflash_output = find_common_tags(articles)  # 2.88μs -> 2.31μs (24.2% faster)

    articles = [
        {"tags": ["tech", "tech", "news"]},
        {"tags": ["tech", "tech", "gadgets"]},
        {"tags": ["tech", "tech", "reviews"]},
    ]
    codeflash_output = find_common_tags(articles)  # 1.66μs -> 1.17μs (41.9% faster)
    # Outputs were verified to be equal to the original implementation


def test_articles_with_mixed_case_tags():
    # Test with articles having mixed case tags
    articles = [{"tags": ["Python", "Coding"]}, {"tags": ["python", "Development"]}, {"tags": ["PYTHON", "Guide"]}]
    codeflash_output = find_common_tags(articles)  # 2.38μs -> 2.09μs (13.8% faster)

    articles = [{"tags": ["Tech", "News"]}, {"tags": ["tech", "Gadgets"]}, {"tags": ["TECH", "Reviews"]}]
    codeflash_output = find_common_tags(articles)  # 1.20μs -> 1.06μs (13.3% faster)
    # Outputs were verified to be equal to the original implementation


def test_articles_with_non_string_tags():
    # Test with articles having non-string tags
    articles = [
        {"tags": ["python", 123, "coding"]},
        {"tags": ["python", "development", 123]},
        {"tags": ["python", "guide", 123]},
    ]
    codeflash_output = find_common_tags(articles)  # 3.04μs -> 2.33μs (31.0% faster)

    articles = [{"tags": [None, "news"]}, {"tags": ["tech", None]}, {"tags": [None, "reviews"]}]
    codeflash_output = find_common_tags(articles)  # 1.67μs -> 1.19μs (40.4% faster)
    # Outputs were verified to be equal to the original implementation


def test_large_scale_test_cases():
    # Test with large scale input where all tags should be common
    articles = [{"tags": ["tag" + str(i) for i in range(1000)]} for _ in range(100)]
    expected_output = {"tag" + str(i) for i in range(1000)}
    codeflash_output = find_common_tags(articles)  # 382ms -> 3.44ms (11025% faster)

    # Test with large scale input where no tags should be common
    articles = [{"tags": ["tag" + str(i) for i in range(1000)]} for _ in range(50)] + [{"tags": ["unique_tag"]}]
    codeflash_output = find_common_tags(articles)  # 189ms -> 1.69ms (11061% faster)
    # Outputs were verified to be equal to the original implementation
from codeflash.result.common_tags import find_common_tags


def test_find_common_tags():
    find_common_tags([{}, {}])


def test_find_common_tags_2():
    find_common_tags([])
🔎 Click to see Concolic Coverage Tests
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_lnp43tks/tmpdki95lvw/test_concolic_coverage.py::test_find_common_tags 1.80μs 1.77μs 1.69%✅
codeflash_concolic_lnp43tks/tmpdki95lvw/test_concolic_coverage.py::test_find_common_tags_2 672ns 491ns 36.9%✅

To test or edit this optimization locally git merge codeflash/optimize-pr1191-2026-01-28T18.02.56

Suggested change
common_tags = articles[0].get("tags", [])
for article in articles[1:]:
common_tags = [tag for tag in common_tags if tag in article.get("tags", [])]
return set(common_tags)
common_tags = set(articles[0].get("tags", []))
for article in articles[1:]:
common_tags.intersection_update(article.get("tags", []))
return common_tags

Static Badge

22 changes: 22 additions & 0 deletions tests/test_common_tags.py
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from codeflash.result.common_tags import find_common_tags


def test_common_tags_1() -> None:
articles_1 = [
{"title": "Article 1", "tags": ["Python", "AI", "ML"]},
{"title": "Article 2", "tags": ["Python", "Data Science", "AI"]},
{"title": "Article 3", "tags": ["Python", "AI", "Big Data"]},
]

expected = {"Python", "AI"}

assert find_common_tags(articles_1) == expected

articles_2 = [
{"title": "Article 1", "tags": ["Python", "AI", "ML"]},
{"title": "Article 2", "tags": ["Python", "Data Science", "AI"]},
{"title": "Article 3", "tags": ["Python", "AI", "Big Data"]},
{"title": "Article 4", "tags": ["Python", "AI", "ML"]},
]

assert find_common_tags(articles_2) == expected
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