Reducing Risk From Legacy Code Through Refactoring

The Perils of Legacy Code

Legacy code refers to older software systems that have accumulated over time. As requirements change and new features get added, legacy code tends to decay, accumulating technical debt. The lack of automated tests and outdated architectures make these systems brittle, risky, and difficult to maintain.

Some of the key perils of legacy code include:

  • Technical debt accumulation – New code gets layered on top of old code without refactoring, leading to decay
  • Brittleness – Code depends on hidden assumptions that break easily when changed
  • Lack of tests – No safety net to catch regressions when making changes
  • Difficulty adding new features – Hard to add new capabilities without breaking existing functionality
  • Slow velocity – Changes require tedious manual testing to avoid breakage

These factors slow down developers and lead to outages in production. Ultimately, the brittle complexity makes legacy systems very risky and costly to maintain. Companies defer necessary improvements due to fears of breaking legacy systems.

Strategies for Safely Improving Legacy Code

Thankfully, legacy code can be improved through incrementally introducing better patterns and automated tests. Here are some effective strategies:

  • Incremental refactoring – Make small changes to improve code structure without changing external behavior
  • Adding tests around untouched code – Increase test coverage to enable future refactoring
  • Strangling legacy code – Build new interfaces around legacy systems and gradually replace internals

The key is to avoid risky “big bang” rewrites and instead take gradual steps to improve quality and capability. Each change should provide incremental value and minimize risk.

Incremental Refactoring

Refactoring means restructuring code to improve its internal structure without changing external behavior. For example, extracting a chunk of code into a well-named function. By incrementally refactoring, developers can pay back technical debt and improve maintainability.

Here are some patterns to enable safe refactoring of legacy code:

  • Introduce interfaces to decouple components
  • Break code into smaller functions with single responsibilities
  • Move logic between components and layers

Small, incremental refactoring is preferred so that each change introduces minimal risk on its own.

Adding Tests Around Untouched Code

Ideally legacy code would have automated test coverage to enable safe changes. When that is not the case, teams should prioritize adding test cases before making structural changes. These test suits create a safety net to catch unintended breaks.

Use techniques like equivalence partitioning to design test cases around untouched but risky modules. Expand test coverage incrementally, paying special attention to integration points between components.

Strangling Legacy Code

“Strangler” refers to an incremental pattern for rewriting legacy systems. The steps include:

  1. Identify a computationally intensive component to replace
  2. Build a new component with modern coding practices
  3. Create a facade interface layer to interact with old and new components
  4. Route traffic to the new component and away from the legacy version
  5. Eventually retire the legacy component entirely

This approach allows systematically decomposing monolithic systems over time. The façade layer shields other components from disruption. By routing traffic incrementally, the team avoids risky “big bang” cut-overs.

Example Code Refactoring

To illustrate incremental refactoring, consider this legacy module with dependencies across global variables and functions:

Before: Tightly Coupled Procedural Code

# Globals
next_id = 1
records = {} 

def add_record(name):
  global next_id
  global records
  records[next_id] = {
    "id": next_id,  
    "name": name
  next_id += 1

def delete_record(id):
  global records
  del records[id] 

def save_records():
  global records
  # code to save records

This code works but has poor structure. Everything depends on global state. Functions know too much about internals. It lacks tests.

After: Loosely Coupled Object-Oriented Code with Tests

class RecordsManager():
  def __init__(self):
    self.next_id = 1
    self.records = {}

  def add_record(self, name):
    self.records[self.next_id] = {
      "id": self.next_id,  
      "name": name

  def increment_id(self):
    self.next_id += 1
  def delete_record(self, id):
    del self.records[id]
  def save(self):
    # code to save records

# Tests
manager = RecordsManager()
assert len(manager.records) == 1

Now the code encapsulates state within a class. Functions access properties directly without relying on side-effects from globals. This structure lends itself to test cases.

We could further improve with repository layers, API interfaces, and more advanced patterns. But even this incremental improvement toward object orientation improves modularity, testability, and maintainability.

When to Rewrite From Scratch

Despite best efforts to refactor, legacy systems may reach a breaking point where they become too costly to operate and impede progress. Rewriting from scratch can become the better option.

Assessing the Breaking Point

Consider initiating a rewrite when:

  • Weekly incidents require firefighting in production
  • Small changes take too long due to manual test needs
  • Major opportunity costs from being stuck on legacy infrastructure

The risks and costs of the status quo outweigh the risks of starting fresh. But the rewrite still needs to be undertaken carefully.

Setting Up the New System in Parallel

Don’t be tempted to shut down the old system until confident the replacement works. Instead:

  1. Build the new system incrementally while the old stays operational
  2. Utilize strangler facade interfaces where possible
  3. Thoroughly test and validate apis match expected contracts
  4. Route traffic carefully to minimize disruption

Patience is key – it’s better for the cutover to take longer while preserving fallback options.

Transitioning Over Safely

Finally, once ready, production traffic can start utilizing the new system. Have roll back procedures in place in case issues emerge. Incrementally ramp up traffic levels until reaching 100%. Celebrate once the old system can be decommissioned!

Adopting Better Practices Going Forward

Beyond remediating immediate issues, leaders should learn from legacy system challenges and adopt practices to avoid recurrence of similar issues. Some recommendations:

Test-Driven Development

Require developers to write test cases before software implementations. Emphasize testability of all new code.

Continuous Integration

Automate build, test, and deployment pipelines. Avoid massive change sets that are risky to release.

Code Reviews

Inspect all code changes to guard against technical debt and enforce standards.

Automated Analysis Tools

Scan code and pipelines for anti-patterns, vulnerable dependencies, and failures to adhere to standards. Remediate findings by automation if possible.

Instilling these practices for new work can avert creating the next generation of legacy code pitfalls.

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