Legacy system modernization in practice: How to migrate and refactor code and regain control of an IT Project?
Is legacy system modernization a topic you put off for later because the old system “still works”? It sends emails, handles orders, shifts data between modules. The problem is that with each passing month, it starts costing more nerves, money, and invisible team time – only emphasizing the need for an update. New features are implemented more slowly. Bugs reappear in the same places. Knowledge about the system resides in the heads of only a few people, creating risk when phasing out legacy solutions. And when something needs to be changed quickly, developers don’t develop the product—they just put out fires, which is often the result of an outdated monolith.
This is not an isolated incident, but a classic symptom of growing technical debt. Stripe estimated that on average, developers waste over 17 hours per week on technical debt and poor code quality, while McKinsey shows that this type of burden genuinely limits the pace of modernization and innovation.
The most difficult moment comes when the problem stops being purely technical. Legacy begins to block the business. Changes to the system become unpredictable, maintenance costs rise, and managers postpone further initiatives because “we first need to stabilize what we already have” – making a modernization strategy key to this process. McKinsey points out that old technologies slow down innovation, hinder integration with modern channels, raise operational costs, and increase operational risk, especially when the system relies on poorly understood business logic and manual testing. In one described case, a large European bank allocated as much as 70% of its IT capacity just to maintaining legacy systems.
This is precisely where many projects taken over by DevQube begin today. And that is why effective modernization should not start with the slogan “let’s rewrite everything,” but with regaining control. In practice, this means an orderly path: first ownership and access, then an audit, followed by securing system knowledge, and only then the actual migration or refactoring. This order is not bureaucracy—it is a way to mitigate risks, including those associated with outdated systems.
When a legacy system truly becomes a problem
Legacy does not simply mean an “old system,” but often involves high complexity and a lack of compatibility with modern technologies. The problem starts when an organization loses the ability to safely develop its product.
Typical warning signs are usually very specific:
In a DevQube case study on rescuing a system without documentation, the client did not have full access to key resources such as the OVH panel, SMTP, or Firebase, and the code contained inconsistencies, dead fragments, and a high level of technical debt. On top of that, every change carried a high risk of regression because the system, being outdated, lacked tests.
This is a very important observation: until a company regains real control over its environment, there is no point in talking about “peaceful product development.” Ownership of domains, repositories, servers, databases, and external services is the foundation. Without it, any further work is conducted on borrowed ground. Therefore, the first stage of taking over a project should always include an inventory of access, permissions, integrations, and operational responsibilities. At DevQube, such a stage is not an add-on to development, but a prerequisite for a safe start to modernization work.
What a safe migration and modernization path looks like
First, you need to regain control over the project in order to carry out an effective transformation. This process consists of well-thought-out stages:
- Stage 1: Regaining access and control
In practice, this means taking over access to infrastructure, cloud accounts, repositories, technical mailboxes, analytics systems, domains, and external services. Only then can you reliably assess what can actually be maintained, what needs to be secured, and what should be replaced. In the described project, the modernization strategy was a key element to minimize risk. In the DevQube case study, newly created environments were built from scratch, and the client received full administrative rights to the entire infrastructure, which is essential in the context of transformation.
- Stage 2: In-depth audit
The second stage is an in-depth audit. It is not just a code review, but an understanding of the entire organism: architecture, business logic, data flow, integration dependencies, database quality, deployment security, and current product limitations. McKinsey emphasizes that meaningful modernization starts with granular transparency and a thorough identification of assets, data, and their connection to business value. Without such a map, it is easy to mistake symptoms for the cause.
- Stage 3: Recreating knowledge about the system
The third stage is recreating knowledge about the system. In practice, this consists of technical documentation, a description of business processes, an integration catalog, and a “safety net” in the form of tests. In the same case study, following a strategic recommendation, a full set of unit, integration, and acceptance tests was implemented, and complete technical documentation reflecting the final structure of the new system was created. This is the moment when the project stops being a black box.
- Stage 4: Actual modernization
The fourth stage is actual modernization—but only after completing the three previous steps, which are necessary to minimize downtime. And there is no single right answer here, as every situation requires an individual approach to system complexity. AWS describes seven cloud migration strategies, the so-called 7 Rs, including refactoring and replatforming, which are essential in the modernization process. Retire, retain, rehost, replatform, and refactor/re-architect are key elements of a modernization strategy. Microsoft, in its framework, explicitly notes that a simple rehost is mainly good when we care about a fast, low-risk migration with minimal changes, but it does not solve existing architectural or performance problems. Furthermore, Azure warns that problematic workloads should not simply be moved “as is,” because then technical debt moves to the new environment along with the application.
Migration, refactoring, or full rewrite
This is the question clients ask most often. The answer is: it depends on the state of the system, its business role, and the risk of change. If the application is critical but still has a clear architecture, isolatable modules, and sensible data control, a gradual refactoring may be reasonable. Martin Fowler has for years described the Strangler Fig approach here—incremental modernization where investment and return appear gradually and visibly, and the risk is lower than with a single “big bang rewrite.”
However, there are situations where refactoring becomes merely expensive sugarcoating of an outdated problem, highlighting the need for a true transformation using AI. DevQube described a modernization strategy that covers various aspects of system transformation. The audit stated this case very clearly: it showed that superficially modifying the existing system would be highly risky, which is why a complete rewrite of the application in modern technology was recommended. Key here was the combination of several factors at once: lack of documentation, lack of tests, lack of infrastructure control, high technical debt, and broad development requirements from the client. In such a setup, a full rewrite was not a whim, but the most rational way to limit risk and protect the investment.
On the other hand, another DevQube project shows that a complete rebuild can also be an answer to time pressure, provided it is accompanied by a good understanding of the risks. In the case of an agricultural platform, the company was responsible for almost completely rewriting the previous version of the system while maintaining online and offline operation, and the full platform replaced the existing solution in three months. The client emphasized, among other things, early problem detection and the reduction of subsequent rework. This is important because good modernization is not about quickly “writing new code,” but about regaining predictability faster.
Why frontend and UX need to be modernized together with the backend
In many companies, IT system modernization is understood too narrowly. Focusing solely on the backend, databases, and infrastructure can be a mistake because even a more stable system will not bring full value if the user still gets lost in the process, does not trust forms, or encounters unintuitive user paths. Google points out that Core Web Vitals measure real user experience in terms of loading, interactivity, and visual stability, and for good UX it is worth striving for an LCP under 2.5s, INP under 200ms, and CLS under 0.1.
But speed alone is not enough. Usability is needed. Studies by Baymard show that 17% of users abandon a purchase due to a checkout process that is too long or complicated, and 17% due to website errors or crashes. This is a very strong argument for treating frontend modernization not as cosmetics, but as an element of limiting real business losses, especially in the API era.
This theme is very well supported by DevQube’s portfolio, showing how modern approaches such as microservices can bring benefits. In the context of an audit of a food ordering app, understanding the system’s complexity and its compliance with modern standards is key; the team analyzed, among other things, navigation, home page structure, content communication, and form usability, resulting in a detailed report of issues along with their categorization by impact on usability, which is crucial in the context of compliance with new standards. In another project—for a service booking platform—recommendations included simplifying the booking process, better filters, simpler forms, more intuitive payments, and rebuilding the customer panel. At the sales message level, this is a very strong argument: DevQube not only “cleans up code,” but also organizes the functionality of how users and employees use the system.
How to measure return on investment in modernization
The biggest mistake in legacy modernization is that companies treat it as a defensive cost rather than an investment in predictability and growth. Meanwhile, the results can be measured very concretely.
DORA recommends looking at five software delivery performance metrics:
Importantly, their research shows that speed and stability are not mutually exclusive in the long run—top teams achieve good results on both sides simultaneously.
In practice, for a modernization project, it is worth adding product metrics to this: the number of production incidents, infrastructure maintenance costs, time to deploy a new feature, the number of support tickets regarding UX errors, Core Web Vitals results, and conversion rates in key processes. If the system supports sales or bookings, improving user paths, forms, and payments can translate directly into business results, as confirmed by Baymard data and system complexity analysis. DevQube’s case studies in UX/UI audits show how outdated systems can be modernized.
A well-executed modernization yields something more than a “newer stack”—and in particular, it can include a transition to microservices. It gives back control: over infrastructure, schedule, deployment quality, change costs, and user experience. And that means fewer fires, more predictability, and a genuinely faster path to the moment when the team is building value again instead of just defending against outages. McKinsey describes this effect directly: reducing technical debt frees engineers to work on products and services that generate business value.
If you are taking over a project without documentation, aren’t sure who truly controls the infrastructure, or your team is afraid to touch the code because every change risks regression—this is not a signal that “we just need to wait a bit longer.” It is a signal that it’s time to start with an audit of the existing system in the context of transformation and updates. At DevQube, such an audit is the first step toward turning risk into an action plan, and chaos into predictable product development.
