October 7, 2025 ⏱️ 10 min
By Cristi P. (RnD – BacknDB Group)
When someone hears about a legacy system, most people freeze for a second.
And that’s not because it is impossible to work with, but rather because the effort required to understand and overcome the legacy challenges is huge, and, of course, the hidden workarounds that make it the cherry on top. We all know that, right?
Well… That era has ended thanks to AI coding assistants. Let’s break it down.
Overview
Legacy systems are more than “old software”. They come with heavy, ongoing costs that often remain hidden until they become critical. The legacy projects come with several hurdles:
- Maintenance drain: Developers are generally spending more time fixing existing features, rather than building new ones.
- Integration difficulties: Connecting old systems to modern tools, APIs, or cloud services becomes increasingly complex.
- Security liability: Older dependencies and unsupported frameworks are more likely to be at risk.
- Lack of skills: Few engineers still know old programming languages and frameworks. This aspect drives up costs and slows progress.
- Innovation lock: Due to the fact that legacy systems anchor the businesses to the past, they can’t easily pivot, scale, or adopt new technologies.
As a result, teams are stuck in a maintenance trap, spending 70–80% of their time keeping the lights on instead of innovating. While companies may recognize the problem, modernization is not easy because it feels slow and risky. And that’s due to the following factors:
- Fragile dependencies: Changing one little thing in a module can cause unexpected failures in other parts of the system.
- Poor documentation: Most of these systems have little or no technical documentation available, and if anything changes, it could be risky because no one knows the consequence of the change.
- High pressure: These systems often support mission-critical processes and downtime is not an option.
- Long timelines: The timelines to completely rewrite or migrate these systems could be years, and the costs could be huge.
- Uncertainty: Business owners fear putting a large capital investment into an aging system and modernize their system without a clear path to return of investment (ROI).
In perception, the modernization of an old system feels like “changing the tires on a moving car”.
This is where AI coding assistants (like GitHub Copilot, GPT-5, Tabnine, etc.) come into play, emerging just as organizations are struggling with legacy modernization. They provide support in three critical ways:
1. Understanding Code
- AI can explain unfamiliar legacy code.
- It creates summaries, flow charts, and intents descriptions that don’t require any tribal knowledge.
- Developers can get up to speed in a legacy codebase faster.
2. Paths for Modernization
- AI can translate old patterns into modern equivalents.
- It provides scaffolding and recommends refactoring in line with Clean Architecture, microservices, or cloud-native design.
- It automates repetitive boilerplate, freeing human time to make higher-level design decisions.
3. Developer Productivity & Confidence
- AI lets developers experiment safely — they can ask “what happens if I change this?” and quickly get outcomes that help them make decisions faster.
- Automated test created faster provides confidence when refactoring fragile legacy code.
- It acts as 24/7 pair programmer that never tires, lowering the barrier to engaging with intimidating old systems.
The Legacy Challenge
As already mentioned in the introduction, legacy systems create many difficulties. Of these, three appear most frequently and have the greatest impact:
Outdated tech stacks and fragile systems – Legacy systems are still based on decades-old technologies that are no longer being actively supported. The obsolete frameworks, fragility of the system, compatibility issues with the new needs and the slow performance of the system are the nightmare of any developer and business owner.
Limited documentation and talent pool – Adapting legacy code is often much harder than just writing new code from scratch. The lost knowledge, as the original developers have retired or left, the sparse and outdated documentation, while the learning curve is slow and the talent scarcity aspect, create a chaos environment for the new developers.
Maintenance trap: throttling innovation – Organizations end up stuck in a cycle of keeping old systems alive instead of moving forward. Most of a developer’s time is spent on bug fixes and patches, while there is less time for building new features, experimenting, or adopting new technologies. The modern demands (cloud, AI, mobile) can’t be met quickly because the core systems hold everything back, while the developers may feel frustrated, leading to low morale and higher turnover.
The challenge can be summed up as follows: legacy systems are too critical to discard but too fragile to evolve. Every bug fix or feature request can become a guessing game while businesses fall behind competitors because innovation is blocked by legacy debt.
AI Coding Assistants
AI coding assistants are applications that utilize large language models (LLMs) trained on a large amount of code and documentation. They can assist developers in several ways:
- Contextual help: They can understand and follow the code you’re working on and suggest contextually relevant completions, explanations, or fixes.
- Natural language queries: Developers can ask simple questions, in plain-English, like: “What does this function do?” or “Convert this code to C#”.
- Refactoring support: They can suggest cleaner, more modern patterns, or help in converting the code from one framework to another.
- Helping create tests faster: They can help write unit tests or integration tests for fragile legacy code by making it safer to touch.
- They are always available: The AI coding assistants act as equally tireless pair programmers, providing instant feedback, suggestions, and other helpful support.
It is important to note though, that these tools do not replace developers. They make developers more productive, allowing for productivity improvements by reducing the grunt work involved in understanding and maintaining code.
AI coding assistants matter now more than ever, arriving with far more context, at a time of incredible pressure on companies:
- Looming legacy crunch: Companies have long been running their core systems on technology that is aging, and they’re starting to feel the impact of rising costs of maintenance.
- Developer shortage: Fewer engineers know legacy stacks, with a huge surge in work to modernize legacy systems.
- Complexity is rising: Modern applications involve cloud, APIs, security, and distributed systems — the complexity is simply too high for human developers to manage.
- AI has matured: The generation of AI models we are currently seeing is finally powerful enough to parse and explain real-world messy codebases.
- Economic pressure: Companies need faster decisions on tighter budgets — efficiency is no longer a nice-to-have.
Given that, it can be said that timing is everything, and fortunately, AI coding assistants have arrived just as companies can no longer afford to ignore legacy problems.
From Chaos to Harmony
A significant aspect of working with legacy code is simply understanding what the code is meant to accomplish. AI coding assistants help make this easier. Using AI allows legacy code to be less of a mystery and more of a map you can navigate confidently.
Modernization of legacy systems is typically years of work, but AI is capable of breaking the complexity barriers. Instead of full rewrites from scratch, AI enables incremental, safer, and faster modernization while boosting the developers’ productivity and confidence. Here is how:
- Plain-language explanations: The AI can explain even complex functions, classes, or entire modules in plain language.
- Code summarization: The AI could summarize long, ugly code blocks into a short statement of intent and the logic flow.
- Dependency mapping: The AI can identify how all the different pieces connect together, reducing the “black box” feel of legacy systems.
- Knowledge transfer: A new developer can ramp up far quicker by asking the AI as opposed to searching for documentation that is most likely out of date.
- Pattern Translation: Over time, AI may suggest relevant modern equivalents for outdated patterns.
- Refactoring support: It proposes cleaner, modular structures aligned with modern best practices like Clean Architecture.
- Language migration: There are times when translating code, across languages is less error, if accomplished with additional help from AI.
- Safer transitions: The time dedicated to the automated test creation of your existing code provides reassurance that changes will not break mission-critical business workflows.
- Less cognitive load: Developers can spend more time solving problems instead of spending time trying to interpret code.
- Real-time feedback: AI provides feedback as a reviewer, helps catch errors, and acts as a mentor by suggesting improvements.
- Confidence in changes: With AI help in writing tests as well as code explanations, developers are more willing to “touch” fragile systems.
- Morale boost: Developers feel empowered, which can prevent turnover and burn out since they are not left feeling drained.
AI coding assistants are changing the experience from “walking on eggshells” with legacy code to “working with confidence”.
Real World Impact
Safer refactoring with tests done with the help of AI
As we already concluded, refactoring a legacy system is risky. If you make a small change, it could have far-reaching impacts, resulting in failures across the system. AI-powered coding assistants can mitigate the risk when refactoring. AI allows developers to treat legacy systems as a safer environment for making bolder changes, avoiding the need to wait for tests to run after every change.
Faster onboarding for new developers
In the legacy system world, ramping up new hires will traditionally take months. AI can significantly reduce that time. AI serves as a kind of knowledge mentor that can successfully support onboarding, transfer knowledge from experienced developers to terminals, and elevate team productivity.
Unlocking pathways to cloud and modern platforms
Modernization of legacy systems is about shifting from simply rewriting code to enabling new business capabilities. This is where AI comes in. AI will flip legacy systems as technology and mission constraints into enabling components for digital transformation.
Turning Point
Organizations today are facing an unprecedented combination of pressures and opportunities:
- Legacy systems strained: Most critical applications are decades old, becoming increasingly fragile and expensive to maintain.
- Increasing business demand: Companies require a faster transformation to digital services, cloud-based services, and mobile-first deliveries.
- AI is ready: Modern AI coding assistants can now understand, explain, and help in refactoring complex codebases.
- Timing coincidence: The technology has matured at the same time that legacy systems are bottlenecks at their maximum level.
AI coding assistants don’t just provide convenience; they have quantifiable business value:
- Reduced costs: Less time spent on maintenance and debugging means a direct reduction in operating costs.
- Faster feature delivery: Teams can modernize and deploy new features, providing increased competitive advantage.
- Reduced risk: Refactoring and automated tests through AI have ensured less likelihood of expensive system failures.
- Leverage talent: AI helps existing teams manage the legacy workload even with a shrinking pool of developers that know old frameworks.
- Strategic positioning: Companies that adopt early-mover AI gain both technological and organizational agility, making future innovations relatively easy.
AI is no longer an option for modernizing and competing as a business.
Evaluating the Future
AI coding assistants are transforming modernization from a challenge into an adventure, turning legacy systems from a burden into an advantage. Legacy modernization is no longer about just maintenance — it is a chance to innovate where AI can help organizations derive embedded value, speed up change, and make old and new systems work together better.
The “legacy challenge” is not solely reliant on the age-old code; it’s about unlocking innovation that’s stuck behind outdated systems. AI coding assistants are the enabling technology to transform modernization from a concept to reality at scale finally, through:
- Providing context on the past
- Automating the present
- Scaling up for future changes
Îndemnul nostru
Efortul pus în programele pentru studenți completează teoria din facultate cu practica care “ne omoară”. Profitați de ocazie, participând la cât mai multe evenimente!
În cei 3 ani am lucrat la proiecte diverse ce au contribuit decisiv la evoluția mea profesională. Am colaborat strâns cu echipe remarcabile, a căror dedicare și sprijin au fost fundamentale în buna dezvoltare a lucrurilor. Cu fiecare proiect, am învățat nu doar aspecte tehnice, ci și abilități esențiale de comunicare și colaborare, reflectate în experiența mea privind interacțiunile cu clienți și modul în care o echipă eficientă operează. Aceasta este cultura companiei, de la ritmul uneori alert al zilei de lucru și practicile Agile, până la momentele relaxante de socializare cu colegii în pauzele de cafea și activități recreative precum darts și ping pong.
Am făcut cunoștință cu echipa NetRom prin intermediul programelor Internship și NetRom Software Academy, in care am descoperit că noțiunile învățate acasă au totuși aplicabilitate, chiar în cadrul unor aplicații reale. Acele experentie m-au convins să aplic pentru un job.
Mentorii foarte deschiși și colegii cu experiență care mi-au răspuns prompt la toate neclaritățile au transformat provocările în task-uri rezolvate cu succes împreună. Făcând parte din #teamnetrom mi-am depășit frica de a greși și am evoluat într-un mod echilibrat pentru mine.
Am aflat de programele NetRom de la o prezentare organizată în facultate de companie. Am decis să mă înscriu la Summer Camp din dorința de a înțelege ce anume ar presupune o cariera în IT. În urma acestui stagiu de practică, am reușit să dezvolt propria aplicație cu ajutorul mentorilor care mi-au oferit informații teoretice, dar mai ales practice, pe care nu aș fi putut sa le găsesc într-un tutorial aleatoriu de pe internet. Modul meu de abordare a aplicaților s-a schimbat odată ce am înțeles în ce constă mai exact procesul, ce presupune un mediu real de lucru și importanța muncii în echipă.
NetRom Software Testing Academy a fost punctul de plecare pentru o carieră pentru mine. Pe parcursul sesiunilor am avut oportunitatea să cunosc bazele necesare pentru asigurarea calității unui produs, urmate de o zi în companie unde am văzut teoria pusă în practică.
Recomand tuturor celor care își doresc o carieră în QA să ia în considerare această academie, fiind destinată atât celor din domeniu, dar nu numai.
Am descoperit NetRom prin intermediul unui program de internship și am fost impresionată încă de la început de mediul de lucru plăcut, de susținerea și angajamentul colegilor. A fost o oportunitate deosebită de a lucra într-o echipă de proiect, cu roluri specifice, și de a înțelege mai bine cultura companiei. Întreaga perioadă s-a dovedit o experiență valoroasă, atât din punct de vedere al cunoștințelor acumulate, cât și prin prisma relațiilor dezvoltate. În momentul în care mi s-a propus să continui ca angajat permanent, a fost o decizie ușor de luat, bucuroasă fiind să pot aplica cele învățate, dar și să adaug cunoștințe noi în fiecare zi.
