In the software lifecycle, there always comes a moment when code needs a review. It can happen, for example, that during the project's growth, temporary solutions were adopted that need fixing, or that time and modifications have made the code too complex. In these cases, code refactoring comes into play: a practice often underestimated, but fundamental for maintaining applications that are efficient, high-quality and sustainable over time.
Code refactoring is the process of restructuring existing source code without changing its external behavior. It's not about fixing bugs, as happens in debugging, nor about introducing new features: its goal is to improve the internal quality of the software, making it simpler to understand, test and evolve over time. In other words, it's an investment in code health, not a cosmetic intervention.
The real impact of refactoring emerges when we observe it as a strategic lever for business. Every application accumulates technical debt over time (those necessary quick choices or temporary solutions that, if not managed, eventually start to slow down development). Through systematic refactoring, this debt is reduced, freeing up resources and time to focus on innovation.
Cleaner and more coherent code allows teams to work with greater speed and confidence, fostering organizational agility, that is, the ability to respond quickly to new market or customer needs. Moreover, clear and easily understandable code translates into a reduction of regression risks (that is, the risk that code modifications damage other functionalities or introduce new bugs) and a reduction in lead time for each new release.
But refactoring, as we'll explore further in the article, is not just a technical practice, it's also a key element in keeping development activities sustainable and fast, in situations where code quality becomes a growth accelerator, and not, as sometimes happens, an obstacle.
Doing refactoring is not a one-shot activity but rather a series of targeted and systematic interventions that allow, over time, to simplify the complexity of the codebase and make it more easily evolvable over time.
One of the most widespread techniques is Composing Method, which consists of breaking down functions or methods that are too long into smaller, more readable blocks. Let's take an example. Imagine a function that, in the context of an e-commerce application, handles calculating the total price of an order: it applies any discounts, adds taxes and finally returns a report. If all these operations are enclosed in a single long and complex function, understanding the role of each step becomes difficult both for those writing the code and for those who will need to modify or test it in the future. Through refactoring, each operation becomes a distinct function, with a clear name and precise responsibility. The result is code that's more linear, clearer and more easily isolatable for a potential unit test.
Another essential technique is conditional simplification, which aims to make decision logic more readable. Nested expressions like "if X is true and Y is false or Z is null" not only slow down reading, but increase the probability of errors. Refactoring in this case leads to introducing intermediate variables with explanatory names, or replacing complex conditions with methods that clearly express the intention: "isEligibleForDiscount" is much more immediate than a long chain of logical operators.
Finally, with Abstract Refactoring we intervene on duplicated elements or common concepts distributed in the code, extracting them into a reusable class or function. For example, if different parts of the application calculate a commission or validate an input in similar ways, creating a shared abstraction reduces redundancy and simplifies future maintenance. Every modification thus becomes centralized and coherent throughout the system, strengthening the overall stability of the application.
Artificial intelligence is becoming a valuable ally in code refactoring as well, and among the most effective tools there's undoubtedly GitHub Copilot: an assistant that interprets the code context and proposes cleaner, more efficient or idiomatic rewrites. Used methodically, Copilot can significantly accelerate software maintenance and improvement work.
Do you already know this tool? Whether the answer is yes or no, we suggest diving deeper with the workshop: So you think you know Copilot?
But how does Copilot concretely integrate into the refactoring process? The first step consists of understanding the existing code, leveraging Copilot Chat commands like /explain. This function generates a textual explanation of what a code block does, clarifying intents, dependencies and potential critical points. It's a quick way to orient yourself in complex or poorly documented code portions, before intervening. You can also ask to add comments to the code to make it clearer even in future readings.
Once the necessary understanding is acquired, it's possible to ask for generic improvement suggestions, for example with prompts like "How can I make this method more readable?" or "Is there a more efficient way to handle this logic?". Copilot will propose alternatives that respect the semantics of the original code, but simplify its structure or improve its performance.
Finally, the real value emerges when moving to targeted refactoring instructions. You can explicitly ask "Reorganize this code applying the Composing Method pattern" or "Extract the validation logic into a separate function". Copilot will generate a refactored version consistent with the request, keeping the software behavior intact.
It's fundamental however to remember that Copilot is an intelligent assistant, not a human substitute. Proposals must always be reviewed, understood and validated by the development team. The expert judgment of the programmer remains the balance point between automation and quality. Because, on closer inspection, every refactoring must not only be technically correct, it must also respect the architecture and business objectives.
Speaking of artificial intelligence, have you already downloaded our white paper?
In the White Paper you'll find all the potential of AI agents, and yes, it also concerns refactoring. Agentic refactoring indeed represents the new frontier in the evolution of AI-assisted development.
This is demonstrated by the evolution of tools like GitHub Copilot itself, which today no longer limits itself to suggesting improvements or code snippets, but now integrates different interaction modes graduated based on task complexity (you'll find everything in our Workshop on Copilot). In particular, if the Ask mode is perfect for brainstorming without altering the code and Edit allows targeted modifications to open files under supervision, it's with the Agent mode that things really change pace.
Agentic systems in fact act directly on the code, operate autonomously on multiple files and autonomously invoke external tools to reach the goal, understanding its context, dependencies and the global structure of the project. AI is no longer just a suggester, but an active collaborator that can execute complex transformations in an autonomous and controlled way.
In this scenario, Claude Code comes into play, one of the most advanced tools currently available for coding and agentic refactoring. Based on a next-generation language model and integrated with the Model Context Protocol (MCP), Claude Code is able to access semantically the entire codebase of a project. Thanks to MCP, the agent truly understands the application context: it knows how the different parts of the software connect to each other, which modules depend on others and where to intervene without compromising system stability. This makes deep refactoring possible - such as the reorganization of entire components or the replacement of architectural patterns - while maintaining code safety and coherence.
As Enrico Zimuel emphasized in our event "Talk on my machine: GenAI x business": the ability of AI to understand code written by other developers (and to explain it to other developers) is the first crucial step toward truly effective collaboration between human and machine. Agentic refactoring is born precisely from this deep understanding: no longer a set of specific suggestions, but an intelligent process that interprets the team's intent and translates it into concrete actions on the code, accelerating the development cycle and increasing the overall quality of the software.
Understanding when to do refactoring is as important as knowing how to execute it. Not every moment is suitable, and intervening in the wrong way can transform a good intention into a risk for project stability. Thinking of refactoring as a form of preventive maintenance helps to insert it into the development cycle with criteria, avoiding perfectionist drifts or waste of time.
When to do it: the ideal moment is before adding new features, especially if these need to integrate with dated or barely readable code. Refactoring at this stage allows building on solid foundations, reducing the probability of introducing bugs. Also after launching a project is an excellent occasion: once the software is in production, the team has a clearer vision of problematic areas and can optimize them in a targeted way. Another frequent case is when a module becomes excessively complex or difficult to maintain; in this scenario, refactoring serves to restore linearity and favor collaboration between teams.
When to avoid it: refactoring should not be tackled under pressure of tight deadlines. In those cases, the goal must remain the delivery of functional value, not code perfection. It's also discouraged on obsolete software or software destined for dismissal: improving something that will soon be abandoned means investing time without return. Finally, it should never be started if the code is not covered by tests: without a safety net, every modification risks introducing regressions that are difficult to detect.
A good criterion, ultimately, is to consider refactoring as a measurable investment: it should be executed when the benefits in terms of stability, development speed or technical debt reduction clearly exceed the cost of the intervention.
Refactoring is not just a code cleanup practice, but one of the central levers of a broader path: Application Modernization. We're talking about the strategic path that allows evolving IT architectures toward greater efficiency, scalability and innovation. (If you want to explore this process in depth, read the in-depth article Application modernization: what it is and what are the benefits).
When an organization decides to evolve its digital ecosystem, it doesn't just optimize what already exists: it defines an overall strategy to make applications more agile, scalable and ready for change. In this context, refactoring becomes the balance point between preserving existing value and the push toward innovation.
Among modernization strategies, there's also replatforming, often the ideal choice for those who want to migrate from legacy applications to a new cloud platform, without having to rewrite all the software from scratch. (To learn more: Replatforming: from legacy to a new cloud ecosystem (+ examples)
Regarding app modernization, at SparkFabrik we adopt a structured approach based on the 5 Rs framework: Rehosting, Refactoring, Rearchitecting, Rebuilding and Replacing. Each "R" represents a different level of intervention, from simple movement of the application to a cloud infrastructure to its complete reconstruction. Refactoring, in particular, occupies an intermediate position: it allows improving the architecture and code without disrupting the overall functioning of the system, making it more maintainable, performant and ready for further evolutions and for cloud optimization.
In this sense, refactoring is often the first concrete step toward modernization, the one that prepares the ground for deeper initiatives like containerization or transition toward a microservices architecture. It's the phase in which quality is sown that will allow subsequent transformations to take root in a stable and lasting way.
To explore our approach in depth and discover how the 5 Rs guide modernization strategies at SparkFabrik, visit the page dedicated to the Application Modernization service.
We've already mentioned technical debt, which is the IT equivalent of a variable-rate loan: it allows gaining speed in the short term, but generates interest that becomes increasingly heavy over time. Every shortcut taken in development (duplicated logic, an outdated dependency, a poorly scalable structure) accumulates until it slows down the pace of innovation and increases maintenance costs. Teams find themselves dedicating more time to understanding and correcting code than to developing new functionalities, with a direct impact on productivity and time-to-market.
Refactoring is the main tool for reducing this debt in a systematic and sustainable way. By improving the internal quality of the software, it increases its readability and coherence, reducing errors and simplifying future evolution. In business terms, it means cutting down the hidden costs of development (those related to complexity and slowness) and returning to the team the ability to innovate quickly. It's an investment that doesn't produce immediate value, but builds the foundations for more solid technological and organizational growth in the long term.
Modernizing applications doesn't just mean updating technology, but realizing a strategic transformation that aligns your company's digital assets with business objectives, making it more flexible, scalable and ready for future challenges. Tackling this transition requires specialized competencies and, above all, requires an overall vision that knows how to unite cloud-native architectures, DevOps practices, process automation and modular and API-first design strategies.
At SparkFabrik we work with a structured approach, guided by application modernization best practices and our experience in the field. We follow every phase of the journey: from rehosting to the adoption of microservices, artificial intelligence integration and UX/UI redesign. The goal? Transform legacy applications so they truly respond to business needs.
Want to see how this approach can make a difference in your company too? Explore our application modernization services or write to us: sometimes a new point of view changes the direction of the project.