The Great Modernization Shift: From Automated Refactoring to AI-led Re-Engineering

Introduction

In the evolving landscape of legacy modernization, a quiet revolution is unfolding, and automated refactoring (tool-based code translation) is steadily giving way to AI-led re-engineering. Automated tool-based refactoring has been hailed as the preferred approach for modernization, valued for its speed and cost-effectiveness. AI-led reengineering is reshaping that narrative, offering smarter and more scalable transformations at comparable costs, times, and efficiencies.

AI is no longer just optimizing the “how” of modernization, it’s redefining the “why” and “what.” Through intelligent code comprehension, self-learning models, and autonomous architecture mapping, AI-led re-engineering doesn’t just migrate legacy applications; it reimagines them for scalability, resilience, and speed in a digital-first economy. The result is not incremental improvement, it’s systemic reinvention.

Modernization strategies are often categorized using frameworks like the 5R, 6R, or 7R models, which outline various migration and transformation paths. The most adopted approaches for Mainframe modernization are:

• Rehost: Migrate legacy applications to modern infrastructure, including cloud platforms, using a ‘lift and shift’ approach without altering the application code.
• Refactor: Perform a line-by-line syntactic conversion or transliteration of legacy source code into a modern programming language, such as transforming COBOL code into Java.
• Reengineering: Analyze and understand the legacy application’s functionality and business logic to design and architect a new solution that meets the same business requirements.

The following mapping illustrates how decisions regarding the target disposition of legacy applications were traditionally guided by a combination of two out of three key factors: cost, quality, and time-to-value. In most cases, approaches such as rehosting and refactoring were commonly selected when lower costs and shorter timelines were prioritized.

In contrast, reengineering was typically considered when the emphasis was placed on achieving higher quality and comprehensive modernization.

Although reengineering promised the highest quality outcomes, it was often deprioritized due to its extended timelines and relatively higher costs.

A Paradigm Shift

With the rise of AI-led reengineering, the cost trade-off is changing. AI is enabling reengineering to be faster, more scalable, and cost-effective. Therefore, it challenges the dominance of refactoring (automated code conversion), which still relies heavily on native libraries and rigid platforms.

In this blog, we examine how AI is redefining modernization, exposing the limitations of refactoring, and positioning AI-based reengineering as the new standard. Through a comparative lens, we highlight how organizations can rethink their strategies in light of this shift.

Automated Code Conversion – Original Value Proposition

Line-by-Line Code Translation: A Transitional Strategy, Not True Modernization

Line-by-line code translation utilizes automated tools to convert legacy languages such as COBOL into modern counterparts like Java, C#, or Python. Commonly referred to as tool-based code conversion, this method retains the original application's structure and logic, resulting in minimal architectural change. It is often favored for its speed, especially when rapid migration is critical, like meeting contractual deadlines or quickly retiring legacy systems.

While recent advancements, including AI-assisted code conversion, have improved the quality of translated code (e.g., cleaner Java output), the underlying architecture remains unchanged. This means the system continues to operate with legacy design principles, thereby limiting its ability to fully leverage modern engineering practices. As a result, this approach represents an intermediate state rather than a destination on the path to true modernization.

The Rise of AI-led Reengineering

Reengineering is modernization in its most valid form; it's not just about upgrading technology but about reshaping how systems align with business goals. It brings a host of strategic benefits that lay the foundation for agility, scalability, and resilience:

• Domain-Driven Design: Aligns architecture closely with business needs for better agility and clarity.
• Monolith to Microservices: Enables scalable, modular systems with faster deployments.
• Event-Driven Architecture: Promotes real-time responsiveness and system decoupling.
• Data Model Modernization: Enhances data consistency, accessibility, and scalability.
• DevOps & CI/CD Foundations: Embeds automation and agility from the ground up.

Economics of Various Modernization Themes

A series of mainframe modernization projects, along with associated cost models, costs for services, licenses, infrastructure, and MLC (Mainframe Monthly License Charge), were analyzed. This led to a strong correlation between benefits vs capital requirements for a particular modernization theme, viz., rehost, refactor, and reengineer.

The mainframe modernization solution spectrum (benefits vs. value) clearly shows that the high cost of traditional reengineering proved to be an entry barrier towards rearchitecting the application, and the low cost of refactor solutions took preference over the ‘right’ modernization.

However, with the rise and advancement in Agentic AI, GenAI technologies, and LLM, reverse and forward engineering have become faster, cheaper, and more accurate. This has led to a paradigm shift in favor of AI-led reengineering over refactoring. The table below compares the two themes across various platforms, considering that the costs and time are now comparable using AI-enabled tools and processes.

Blurring Boundaries

The Shift from Line-by-Line Refactoring in the Age of AI-Driven Modernization

With modernization costs now comparable across both approaches, the core advantage of line-by-line code translation has largely diminished. Organizations increasingly recognize that while this method delivers syntactic upgrades, it fails to evolve the underlying architecture.

This marks a turning point: line-by-line conversion is no longer the preferred path, but rather a transitional tactic being phased out in favor of intelligent, future-ready transformation.

Coforge’s AI Framework – Embracing the Future

AI is rapidly reshaping legacy modernization. What once felt futuristic, low-code/no-code platforms, AI-driven reengineering, automated testing, and tool integration for custom environments, have become essential for Mainframe Modernization. As these capabilities mature, existing processes and new ones will evolve.

Coforge has its own AI-driven, composable platform called Forge-X, which includes workflows for all phases of Legacy Modernization. Coforge keeps automation and intelligence at the heart of every solution it generates for Legacy Modernization and emphasizes the intersection of profound domain ability and emerging technologies (like AI, cloud, and data analytics).