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Description

Related Issue

Types of changes

  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to change)
  • Chore (non-breaking change that does not add functionality or fix an issue)

Checklist:

  • I have read the Code of Conduct
  • I have updated the documentation accordingly.
  • All commits are GPG signed

Summary by CodeRabbit

  • Refactor
    • Improved the format and clarity of error messages in a core function.
    • Enhanced the handling of component trace details for better verbosity control.

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This is a benchmark review for experiment review_of_reviews_20240506.
Run ID: review_of_reviews_20240506/benchmark_2024-05-06T00-17-17_v1-16-0-235-g04856f438.

This pull request was cloned from https://github.com/2lambda123/google-python-fire/pull/5. (Note: the URL is not a link to avoid triggering a notification on the original pull request.)

Experiment configuration
review_config:
  # User configuration for the review
  # - benchmark - use the user config from the benchmark reviews
  # - <value> - use the value directly
  user_config:
    enable_ai_review: true
    enable_rule_comments: false

    enable_complexity_comments: benchmark
    enable_docstring_comments: benchmark
    enable_security_comments: benchmark
    enable_tests_comments: benchmark
    enable_comment_suggestions: benchmark
    enable_functionality_review: benchmark

    enable_approvals: true

  ai_review_config:
    # The model responses to use for the experiment
    # - benchmark - use the model responses from the benchmark reviews
    # - llm - call the language model to generate responses
    model_responses:
      comments_model: benchmark
      comment_validation_model: benchmark
      comment_suggestion_model: benchmark
      complexity_model: benchmark
      docstrings_model: benchmark
      functionality_model: benchmark
      security_model: benchmark
      tests_model: benchmark

# The pull request dataset to run the experiment on
pull_request_dataset:
- https://github.com/gdsfactory/gdsfactory/pull/2714
- https://github.com/gdsfactory/cspdk/pull/31
- https://github.com/gdsfactory/cspdk/pull/32
- https://github.com/allthingslinux/tux/pull/204
- https://github.com/rybalka1/devmetrics/pull/9
- https://github.com/albumentations-team/albumentations/pull/1705
- https://github.com/albumentations-team/albumentations/pull/1706
- https://github.com/BuczynskiRafal/stormwater-analysis/pull/16
- https://github.com/BuczynskiRafal/stormwater-analysis/pull/17
- https://github.com/nbhirud/system_update/pull/26
- https://github.com/nbhirud/system_update/pull/27
- https://github.com/nbhirud/system_update/pull/30
- https://github.com/nbhirud/system_update/pull/29
- https://github.com/nbhirud/system_update/pull/28
- https://github.com/writememe/motherstarter/pull/241
- https://github.com/gdsfactory/gdsfactory/pull/2715
- https://github.com/Anselmoo/spectrafit/pull/1282
- https://github.com/Remi-Gau/bids2cite/pull/84
- https://github.com/2lambda123/google-python-fire/pull/5
- https://github.com/2lambda123/google-python-fire/pull/6
- https://github.com/2lambda123/google-python-fire/pull/11
- https://github.com/2lambda123/google-python-fire/pull/14
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/1
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/3
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/5
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/7
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/8
- https://github.com/2lambda123/analogdevicesinc-hdl/pull/9
- https://github.com/2lambda123/deepscan-vscode-deepscan/pull/2
- https://github.com/2lambda123/ultralytics-ultralytics/pull/3
- https://github.com/2lambda123/ultralytics-ultralytics/pull/4
- https://github.com/2lambda123/ultralytics-ultralytics/pull/7
- https://github.com/2lambda123/ultralytics-ultralytics/pull/8
- https://github.com/2lambda123/ultralytics-ultralytics/pull/10
- https://github.com/2lambda123/ultralytics-ultralytics/pull/11
- https://github.com/2lambda123/mcafee2cash/pull/6
- https://github.com/2lambda123/OpenBioLink-ThoughtSource/pull/1
- https://github.com/2lambda123/OpenBioLink-ThoughtSource/pull/5
- https://github.com/kod-kristoff/parallel-corpus-rs/pull/4
- https://github.com/ANIALLATOR114/API-Artisan/pull/2
- https://github.com/ignition-api/8.1/pull/273
- https://github.com/New-dev0/SpotifyIG/pull/1
- https://github.com/supabase-community/postgrest-py/pull/425
- https://github.com/supabase-community/auth-py/pull/488
- https://github.com/LLotme/vscode-surround/pull/4
- https://github.com/Nuitka/Nuitka/pull/2837
review_comment_labels:
- label: correct
  question: Is this comment correct?
- label: helpful
  question: Is this comment helpful?
- label: comment-type
  question: Is the comment type correct?
- label: comment-area
  question: Is the comment area correct?
- label: llm-test
  question: |
    What type of LLM test could this comment become?
    - 👍 - this comment is really good/important and we should always make it
    - 👎 - this comment is really bad and we should never make it
    - no reaction - don't turn this comment into an LLM test

# Benchmark reviews generated by running
#   python -m scripts.experiment benchmark <experiment_name>
benchmark_reviews: []

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Hey @sourcery-ai-experiments-bot - I've reviewed your changes and they look great!

Here's what I looked at during the review
  • 🟢 General issues: all looks good
  • 🟡 Security: 1 issue found
  • 🟢 Testing: all looks good
  • 🟢 Complexity: all looks good

LangSmith trace

Help me be more useful! Please click 👍 or 👎 on each comment to tell me if it was helpful.

if not callable(serialize):
raise FireError("serialize argument {} must be empty or callable.".format(serialize))
raise FireError(
'The argument `serialize` must be empty or callable:', serialize)

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🚨 suggestion (security): Consider removing the variable from the error message for security reasons.

Exposing variable content in error messages can lead to security risks, especially if sensitive data is inadvertently logged.

Suggested change
'The argument `serialize` must be empty or callable:', serialize)
raise FireError(
'The argument `serialize` must be empty or callable.')

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Is this comment correct?

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Is this comment helpful?

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Is the comment type correct?

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Is the comment area correct?

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What type of LLM test could this comment become?

  • 👍 - this comment is really good/important and we should always make it
  • 👎 - this comment is really bad and we should never make it
  • no reaction - don't turn this comment into an LLM test

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4 participants