Legacy codebases are the backbone of many successful businesses. Modernizing these systems is a priority for businesses to stay competitive today.
The tricky part? Knowing whether the risks of modernization will be worth the reward. Legacy systems slow progress, making it harder to support key business goals.
In the webinar “A roadmap to working with your legacy codebase,” hosted by LeadDev, a panel of experts shared actionable strategies to address these challenges. They explored how to identify problem areas, leverage AI, and align engineering with business priorities. Their insights offer a clear roadmap for transforming legacy systems into a strong foundation for future growth.
What makes code “legacy”?
“Sometimes the tech stack is solid, but the application logic has grown outdated. Other times, the tech is rusty, while the functionality shines. And if both are rusty? That’s very legacy.”
– Plum Ertz, Senior Director, Engineering at Ro
Legacy code emerges when either the technology or the business logic—or both—can no longer keep up with current needs. Outdated stacks or overly narrow assumptions can quickly turn once-functional systems into bottlenecks.
“The riskiest legacy code is often undocumented—no one knows how it works or what it might break”
– Brigitta Böckeler, Global Lead for AI-assisted Software Delivery at Thoughtworks
Code becomes especially problematic when it lacks documentation, leaving teams unsure how to make changes without causing new issues. At this point, modernization is unavoidable.
The challenge of legacy code
“The worst thing we can do is rebuild the thing we don’t like in a different language.”
– Plum
Legacy code often reflects outdated business logic or technical constraints, but rushing to replace it without proper analysis can repeat the same mistakes and leave you in a far worse position than you were in to start. A successful approach involves understanding both the technical and business context before acting.
“In other contexts, legacy is something valuable left behind, but in software, legacy often signals the need for cleanup or improvement.”
– Brigitta Böckeler, Global Lead for AI-assisted Software Delivery at Thoughtworks
While legacy systems are built on years of effort and expertise, their complexity and lack of upkeep make them a barrier to progress. The key is turning these systems into assets through careful modernization.
Step 1: Understand before acting
“If you skip documenting the existing application, you’re setting yourself up for costly corrections later.”
– Tom Ahi Dror, Co-founder at Swimm
A thorough understanding of the codebase is critical before making any changes. Whether the goal is modernization or maintenance, documenting how the code works—and why—can prevent expensive mistakes down the line.
“When accidental complexity gets out of hand, changes take far too long. That’s when you know it’s time to intervene.”
– Brigitta
Böckeler emphasized using tools like Git history to identify hotspots and risky areas of the code. Establishing “health metrics” rather than focusing on “tech debt” reframes conversations with stakeholders, making it easier to secure buy-in for necessary improvements.
Step 2: Modernize with clear goals
“Is it a 1:1 functional rebuild, or do we rethink how the application serves us today?”
– Plum
Modernization should always start with clear objectives. The team must agree on whether the goal is to replicate existing functionality in a more sustainable form or to redesign elements to better serve current needs.
“Not all parts of the codebase need fixing. It’s about understanding what’s critical and where to invest.”
– Brigitta
Smart prioritization is essential to avoid getting lost in scope creep. Tools that map dependencies or flag frequently updated components can help teams focus their efforts where they matter most.
Step 3: Use AI—Responsibly
“Generative AI can assist in reverse engineering, but it’s no substitute for structured code comprehension.”
– Brigitta
AI offers significant advantages in tasks like summarizing code, automating repetitive syntax changes, and navigating complex codebases. However, it cannot fully replace human expertise, particularly in understanding intricate dependencies or poorly documented legacy systems.
“Static analysis combined with AI works magic—LLMs can translate highly technical insights into readable, actionable documentation.”
— Tom
LLMs are only as knowledgeable as the data they’ve been trained on, that’s why it’s important to use AI in combination with deterministic tools for better results. This hybrid approach is essential when tackling legacy systems with limited or inconsistent documentation.
Product 🤝 Engineering
“Legacy systems and new features aren’t separate tracks—they’re part of the same train. To move forward, you have to clear the obstacles on the tracks.”
– Plum
Effective modernization requires seamless collaboration between product and engineering teams. By aligning technical health goals with product objectives, teams can ensure that both short-term features and long-term investments deliver value.
Avoiding tomorrow’s legacy
“The code we write today is tomorrow’s legacy. Without a strong context layer, we risk losing business logic as code becomes harder to understand.”
— Tom
As development accelerates and teams adopt AI-driven tools, the volume of code is growing faster than ever. Without proper documentation and context, today’s efficient solutions can quickly become tomorrow’s obstacles. Maintaining a clear connection between code and its business purpose is critical to avoid future black boxes.
“If we want to avoid creating future legacy systems, we need to ensure our code is easy to understand, not just for us today but for the teams who will inherit it tomorrow.”
– Brigitta
Building sustainable systems means focusing on readability, documentation, and flexibility. Teams must prioritize practices that make codebases accessible to future developers, ensuring continuity and minimizing long-term risks.
Wrapping up
Legacy code is an inevitable part of software development, but its challenges don’t have to define your team’s progress. By understanding your codebase, setting clear goals, using AI carefully, and ensuring product and engineering teams work together, you can turn legacy systems into assets instead of roadblocks.
Modernizing doesn’t mean starting over. It’s about making thoughtful improvements that support your business now and in the future. Tools like Swimm can make this process easier by helping teams navigate and understand even the most complex legacy codebases.
Ready to see how Swimm can help your team? Book a demo today.
