ruby-optimize

Ruby Optimize Command

Analyzes Ruby code and provides optimization recommendations for performance, memory usage, code readability, and idiomatic Ruby patterns.

Arguments

• $1: path (required) - File or directory path to optimize
• $2: focus (optional) - Optimization focus: performance, memory, readability, or all (default: all)

Usage Examples

# Analyze and optimize all aspects
/ruby-optimize app/models/user.rb

# Focus on performance only
/ruby-optimize app/services/ performance

# Focus on memory optimization
/ruby-optimize lib/data_processor.rb memory

# Focus on readability and idioms
/ruby-optimize app/ readability

# Optimize entire project
/ruby-optimize . all

Workflow

Step 1: Profile and Analyze Code

Discovery Phase:

1. Parse Ruby files in specified path
2. Identify methods and code patterns
3. Detect performance anti-patterns
4. Analyze memory allocation patterns
5. Check for idiomatic Ruby usage
6. Measure complexity metrics

Analysis Tools:

# Use Ruby parser
require 'parser/current'

# AST analysis for pattern detection
ast = Parser::CurrentRuby.parse(source_code)

# Complexity analysis
require 'flog'
flog = Flog.new
flog.flog(file_path)

Step 2: Performance Analysis

Detect Performance Anti-Patterns:

1. Inefficient Enumeration:

# ISSUE: Using each when map is appropriate
def process_users
  result = []
  users.each do |user|
    result << user.name.upcase
  end
  result
end

# OPTIMIZED: Use map
def process_users
  users.map { |user| user.name.upcase }
end

# Benchmark improvement: 15-20% faster, less memory

2. Repeated Object Creation:

# ISSUE: Creating regex in loop
def filter_emails(emails)
  emails.select { |email| email.match(/@gmail\.com/) }
end

# OPTIMIZED: Create regex once
EMAIL_PATTERN = /@gmail\.com/

def filter_emails(emails)
  emails.select { |email| email.match(EMAIL_PATTERN) }
end

# Benchmark improvement: 30-40% faster for large datasets