CodeDrift: AI-Generated Code Quality Analyzer

language: python code_snippet: def process_data(data_list): result = [] for item in data_list: if item is not None: if item > 0: processed_item = item * 2 + 1 result.append(processed_item) return result focus_areas: performance, readability severity_level: medium

## CODEDRIFT ANALYSIS REPORT ### OVERALL QUALITY SCORE: 72/100 ### CRITICAL ISSUES (Severity: High) None detected. ### WARNINGS (Severity: Medium) - **Issue Type**: Inefficient list building pattern - **Location**: Lines 2-7, result.append() in loop - **Impact**: Performance degradation with large datasets due to repeated list resizing - **Fix**: Use list comprehension or pre-allocate list size - **Issue Type**: Nested conditional complexity - **Location**: Lines 4-6, nested if statements - **Impact**: Reduced readability and maintainability - **Fix**: Combine conditions or use early returns ### SUGGESTIONS (Severity: Low) - **Issue Type**: Generic variable naming - **Location**: Throughout function - **Impact**: Poor code self-documentation - **Fix**: Use descriptive names like 'numbers' instead of 'data_list' ### AI-GENERATION INDICATORS - Overly explicit None checking pattern - Medium likelihood - Generic variable names (data_list, item) - High likelihood - Verbose conditional structure - Medium likelihood ### PERFORMANCE METRICS - Time Complexity: O(n) - Space Complexity: O(n) - Estimated Runtime Impact: 15-25% slower than optimized version ### REFACTORING RECOMMENDATIONS 1. Priority 1: Replace with list comprehension for better performance 2. Priority 2: Improve variable naming for clarity 3. Priority 3: Add type hints and docstring ### SUMMARY Code is functionally correct but shows typical AI-generation patterns. Main concerns are performance optimization and readability improvements. Recommended refactor: `return [item * 2 + 1 for item in numbers if item and item > 0]`