Confident Refactoring When Money Is on the Line

Safely Refactoring Critical Payment Code: A Practical Guide

Refactoring code within payment systems presents unique challenges that extend far beyond typical software development risks. In the financial domain, even minor errors can trigger substantial financial losses and irreparable damage to customer trust. This reality creates a paradox where development teams often avoid necessary refactoring, allowing code quality to deteriorate over time.

The psychological barrier to refactoring payment code stems from the high stakes involved. Unlike other domains where bugs might cause inconvenience, payment system failures can directly impact revenue and violate customer expectations. This fear frequently leads to codebases that accumulate technical debt, becoming increasingly complex and difficult to maintain.

The Legacy Payment System Dilemma

Having extensively worked with legacy payment infrastructure, I've witnessed firsthand the technical debt that accumulates when developers avoid touching critical code. These systems often exhibit what I call "spooky windows" - complex, poorly understood code sections that team members avoid modifying due to fear of introducing errors.

This phenomenon mirrors the broken window theory from criminology, where visible signs of disorder in an environment encourage further deterioration. In software development, untested code, mysterious comments, and convoluted logic create an atmosphere where no one feels confident making improvements. The result is a system that gradually becomes more fragile despite appearing to function correctly.

A Case Study: Refactoring a Payment Calculator

Recently, I faced the challenge of extending functionality in a payment calculation module that determined how much customers would be charged. This single module contained years of accumulated complexity, with new features implemented through additional conditional statements that often lacked clear business justification.

The situation was complicated by several factors:

• No comprehensive unit tests existed to document expected behavior
• Only sporadic integration tests provided limited coverage
• The code contained concerning comments like # not sure why we do this and # how can this be zero?

Given the critical nature of this code and its complexity, a traditional test-driven approach would have been impractical. The module had numerous branching paths that would have required extensive documentation to understand fully. Recognizing that maintaining this state was unsustainable, I determined that refactoring was necessary.

Implementing Verify Branch by Abstraction

To mitigate the risks associated with refactoring critical payment code, I employed a strategy known as "Verify Branch by Abstraction." This technique, pioneered by Steve Smith, allows developers to introduce new implementation while maintaining the existing code as a fallback mechanism.

The approach works by placing a toggle in front of the code being modified, which directs traffic to both the original and new implementations simultaneously. The results from both paths are compared, with the original implementation's result being returned unless a difference is detected, in which case an error is logged.

This method provides several advantages over traditional testing approaches:

• It validates the new implementation against real-world production data
• It catches edge cases that might not appear in test environments
• It provides immediate feedback when the refactoring introduces errors

Practical Implementation

The core of my implementation involved creating a new version of the calculator while maintaining the original logic. I structured the code as follows:

def call(input)
  original = OriginalCalculator.call(input)

  if Feature.enabled?("calculator_refactor")
    candidate = CandidateCalculator.call(input)
    compare(original, candidate)
  end
  rescue => e
    log_error(e)
  ensure
    original
end

def compare(original, candidate)
  # compares the attributes of both objects and logs
  # an error if they differ
end

This implementation allowed me to run both code paths simultaneously while always returning the original result for actual transactions. The safety mechanisms of rescue and ensure prevented any unexpected behavior from affecting customers.

Production Testing and Iteration

With both implementations running in production, I observed several discrepancies between the original and refactored code paths. These differences revealed two important issues:

1. A misunderstanding of how a specific discount mechanism was applied
2. An edge case scenario that wasn't apparent during development

After addressing these issues, I maintained the dual implementation for an additional two weeks, during which another subtle bug emerged. This experience reinforced the importance of extended observation periods when refactoring critical financial systems. Ultimately, I waited a full month before completely migrating to the new implementation.

Alternative Tools and Approaches

While my implementation was straightforward, more complex refactoring projects may benefit from specialized tools. The Ruby gem "scientist" provides a robust framework for implementing branch by abstraction with additional features for experiment management and analysis.

I've successfully used scientist to migrate an Elasticsearch cluster from version 1.X to 6.X without any service interruption. The library's clean API makes it straightforward to set up controlled experiments that compare multiple implementations in production environments.

For teams working in other programming languages, numerous alternatives to scientist are available, each adapted to their respective ecosystems.

Conclusion

Refactoring critical payment code doesn't have to be a high-stakes gamble. By implementing Verify Branch by Abstraction and maintaining both old and new implementations simultaneously, development teams can confidently modernize their financial systems while minimizing risk to customers and revenue.

The key to successful refactoring in payment domains lies in embracing a methodical approach that includes:

• Comprehensive testing with production data
• Extended observation periods in production
• Gradual migration strategies
• Continuous monitoring and comparison

These practices transform refactoring from a dangerous endeavor into a manageable, incremental process that improves system reliability without compromising stability.