Gemini Claude Code Review Bugfix Comparison Evaluation — Git skill for Claude Code

AI Code Review and Bug Fix Comparison

**Repository**: [Kosmos AI Scientist](https://github.com/jimmc414/Kosmos) | **Date**: November 19, 2025 | **Codebase**: ~44,700 lines of Python

Abstract

This evaluation compared AI models across two tasks: static code review for bug identification and active bug fixing through code modification. Eleven model configurations were tested on code review, with nine producing measurable results. Seven models participated in bug fixing, with five submitting actual code changes. The highest performers achieved 81.7% bug fix rates (49/60 bugs resolved).

Methodology

Evaluation Framework

The comparison consisted of two independent evaluations:

1. Static Code Review: Models analyzed the Kosmos codebase to identify bugs without execution
2. Active Bug Fixing: Models received a list of 60+ bugs and attempted fixes in isolated git branches

Scoring Criteria

**Code Review Metrics**:

• Bug count with specific line numbers
• Severity categorization accuracy
• Actionability of recommendations
• False positive rate

**Bug Fix Metrics**:

• Bugs successfully resolved (verified by test suite)
• Test pass rate improvement from 57.4% baseline
• Code coverage change from 22.77% baseline
• Code quality (no regressions, proper error handling)

Part 1: Code Review Evaluation

Code Review Prompt

Models received the complete Kosmos repository with instructions to:

• Identify execution-blocking bugs
• Provide specific file paths and line numbers
• Categorize by severity (Critical, High, Medium, Low)
• Suggest actionable fixes

Full prompt available in: `code_reviews/CODE_REVIEW_PROMPT.md`

Code Review Results

Rank	Model	Bugs Found	Line Numbers	Quality Score	Grade
1	CC Sonnet Web	45+	Yes	47/50	A+
2	Gemini Deep Research	10	Partial	38/50	B
3	CC Sonnet	26	Yes	35/50	B-
4	Gemini 3	3	Yes	32