As enterprises accelerate their push to modernize aging and fragmented codebases, the allure of Large Language Models (LLMs) has grown immensely. Touted as transformative tools capable of automating code translation and bridging technology gaps, LLMs are often viewed as a near-magical solution for breathing new life into legacy systems. However, many teams quickly discover a sobering reality: applying LLMs to full-scale, repository-level code migration is far more complex than expected.
Having led multiple modernization initiatives and evaluated a wide range of LLM-powered and traditional approaches, I’ve observed firsthand the patterns that separate successful efforts from stalled or failed ones. While LLMs can offer meaningful acceleration, they are not plug-and-play solutions. Their effectiveness hinges on the broader engineering and strategic framework in which they’re deployed.
Across these efforts, three critical strategies have emerged as essential to success. These are not merely technical best practices—they are foundational principles that help teams navigate the messy, nuanced, and often domain-specific process of transforming legacy systems into modern, maintainable architectures.
Essential Lessons for AI-Powered Code Modernization
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Build custom translation pipelines tailored to each language pair by combining LLMs with compiler-assisted techniques. This hybrid approach consistently achieves automation rates above 90%, far surpassing the sub-10% success seen with general-purpose LLMs alone.
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Apply the “habitable code” principle by modernizing high-impact, high-traffic modules first—those that are critical to the business and frequently touched by developers. Support these efforts with robust integration and regression testing to ensure stability and confidence throughout the transition.
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Design collaborative workflows where AI augments, not replaces, developers. LLMs are ideal for automating boilerplate and repetitive code translations, but human developers—still outperforming AI by nearly 2x in precision and contextual accuracy—are essential for handling edge cases, architectural decisions, and domain-specific logic.
Large Language Models (LLMs) are transforming the software engineering landscape, but when applied to full repository-scale code translation, the gap between promise and performance becomes immediately apparent. Engineering leaders, solution architects, and modernization teams hoping for a seamless lift-and-shift experience quickly encounter the practical limitations of current LLM technologies.
Through direct experience leading language migrations across a wide range of domains—from analytical frameworks to systems-level codebases—I’ve consistently found that general-purpose LLMs break down under real-world pressure. These models struggle with intertwined dependencies, nuanced language semantics, non-trivial build systems, and long-tail bugs. While they perform reasonably well on isolated, synthetic problems, they fall short when evaluated against production-level standards.
The solution is not a universal model that works across all use cases, but rather a deliberate, hybrid strategy: blending LLMs with deterministic compiler tooling, custom static analysis, and domain-informed human oversight. This targeted approach, tailored to specific language pairs and architectural contexts, has already proven capable of exceeding 90% automation across large-scale modernization programs.
This article presents a roadmap built on those lessons. We’ll break down the technical and strategic challenges of repository-scale LLM translation, explore the architectural and process bottlenecks that stall naive implementations, and share proven techniques that work in practice—not just theory. Organizations that adopt these domain-aware, AI-augmented strategies will be best positioned to lead the next wave of enterprise code modernization.
Making Sense of Legacy Code — The Habitability Imperative
Legacy code isn’t just a technical burden—it’s a strategic and economic constraint. The real issue isn’t whether your codebase is pristine, but whether it can evolve fast enough to keep up with market demands and innovation cycles. As Peter Gabriel of ThoughtWorks insightfully framed it through the lens of “habitable code”, the focus shouldn’t be on achieving uniform perfection, but on making the high-use areas of the codebase maintainable. It’s the software equivalent of keeping your kitchen and bathroom clean rather than worrying about cobwebs in the attic.
Wettel and Lanza [10] define habitability in software as “the characteristic of source code that enables programmers, coders, bug-fixers, and people coming to the code later in life to understand its construction and intentions.” This principle lies at the heart of sustainable modernization. Codebases that are logically structured, readable, and predictable lower the cognitive load for developers—making them easier to modify, debug, and extend.
In practice, the challenges of legacy code differ depending on the scale and complexity of the codebase:
Small (S) Codebases
Here, legacy concerns are typically minimal. These systems are often ideal candidates for full rewrites or replacements. This is where microservices shine—not because they’re distributed, but because they allow for tight, bounded scopes (“micro”). The ease of greenfield rewrites is greatest at this level.
Medium (M) Codebases (100–300K LOC)
For systems owned by a single team, a two-pronged modernization approach is necessary:
- Stop the bleeding — prevent new technical debt from entering the system through better dev practices, peer reviews, and quality gates.
- Dig out slowly — incrementally refactor the most critical areas with robust test coverage to prevent regressions.
These codebases require rigorous discipline and automated safeguards to keep modernization on track without interrupting day-to-day delivery.
Large (L) Codebases (multi-team, multi-year)
At this scale, legacy complexity becomes deeply layered. These repositories represent years of accumulated institutional knowledge—business logic woven into code through bug fixes, last-minute patches, and domain-specific edge cases. The challenge becomes archaeological: understanding why things were built a certain way, what trade-offs were made, and where the buried assumptions live. Modernization at this level requires patience, insight, and careful excavation rather than brute-force rewrites.
Extra-Large (XL) Systems (multi-decade, hundreds of contributors)
For truly massive legacy systems, traditional modernization techniques are simply infeasible. No one team—or even group of teams—can safely hold the full system in mind. The only viable strategy is strategic decomposition: carve out autonomous subdomains with strong boundaries, enabling independent teams to modernize incrementally without disrupting global system behavior. These boundaries must be defined with care to preserve performance, compliance, and data integrity.
Legacy code, then, is not a monolith—it’s a spectrum of complexity, organizational memory, and business criticality. Understanding its habitability at every scale is crucial for choosing the right modernization strategy. Whether you’re cleaning up a small utility script or untangling a decades-old mainframe application, embracing the habitability mindset is the first step toward turning legacy code from a liability into a launchpad for transformation.
The Promise and the Current Reality of Automated Code Translation
The business case for automated, repository-level code translation is incredibly attractive. Organizations envision slashing modernization costs, accelerating migration timelines, and dramatically reducing their dependency on niche legacy language experts. In theory, large language models (LLMs) promise a future where entire legacy systems can be upgraded and translated with minimal human intervention, allowing teams to focus on innovation rather than maintenance. However, the current reality falls far short of this vision.
Sobering Benchmarks in Real-World Codebases
Recent empirical studies paint a stark picture. Wang et al. [9] conducted a thorough investigation into LLM-driven repository translation, evaluating models on real codebases. Their findings are telling:
“The best-performing LLM, Claude-3.5-Sonnet, only achieves 7.33% on Success@1 and 12% on Success@3.”
In practical terms, this means that even with three attempts, only a tiny fraction of translated modules successfully compile and pass tests — a dismal return when engineering teams are depending on consistent automation to scale modernization.
Even more troubling is the performance on full-stack, feature-level tasks. The FEA-Bench study [1], which tests model capability in implementing new features within existing repositories, found that:
“The best model (GPT-4o) only achieves 39.5% Pass@1.”
This isn’t a margin-of-error issue. These are real constraints in production environments — systems with tightly coupled modules, implicit architectural assumptions, and nuanced coding idioms. These factors routinely trip up even the most advanced models.
Meanwhile, SWE-Lancer [2] — a benchmark based on over 1,400 real freelance tasks scraped from Upwork — adds another layer of realism. These were projects with actual business impact and budgets totaling over $1 million. Even under these conditions:
- The best LLM (Claude 3.5 Sonnet) completed only 26.2% of individual contributor-level tasks.
- It reached 44.9% for project management tasks.
- The total value of successfully completed work was under $400,000, leaving more than 60% of potential delivery value unrealized.
The Case for Specialized Translation Architectures
My own work building and deploying code translation pipelines across enterprises mirrors these findings. Initial trials using general-purpose LLMs — even with post-processing heuristics — consistently failed to produce reliable, test-passing code across entire repositories. Only when we invested in language-pair-specific architectures, combining LLMs with compiler-based intermediate representations, AST transforms, and domain-aware static analysis tools, did success rates start to climb.
In select cases, such as migrating well-structured analytical Python code to TypeScript, we achieved over 90% automation — but this performance cratered when applying the same architecture to structurally different languages like Java, Go, or C++. These architectures are not one-size-fits-all. Instead, they must be tailored to accommodate the idioms, build systems, type systems, and concurrency models of each source-target language pair.
In short, while LLMs hold great promise, the hype around universal translation solutions is misleading. The benchmarks and field experiences show that general-purpose models struggle with context, integration, and semantic accuracy at the scale modern enterprises require.
Until these limitations are meaningfully addressed, successful repository-level modernization will demand bespoke, hybrid strategies — blending AI with traditional compiler theory, test harnesses, domain expertise, and language-specific tools. Organizations that recognize this and invest in targeted, high-fidelity translation infrastructure will be best positioned to unlock the true ROI of automated code migration — without falling prey to false expectations.
The Vibe Coding Paradox — Accelerating Legacy at the Speed of AI
A new phenomenon is emerging in the age of AI-assisted development: “vibe coding.” Coined in early 2024, this term captures a rapidly growing trend — developers, particularly early-stage founders and non-traditional programmers, are relying heavily on large language models to generate most of their application logic. The appeal is obvious: you describe your intent, and the AI writes the code. Iteration happens in seconds. Applications come to life in days, not months.
But this newfound velocity hides a dangerous paradox: what starts as “vibe coding” inevitably becomes “vibe debugging.”
Y Combinator’s managing partner, Jared Friedman, recently shared that 1 in 4 startups in the Winter 2025 batch have codebases that are 95% AI-generated. These companies are building entire platforms using tools like GPT-4o and Claude 3.5 — with minimal manual intervention. At first glance, this is a dream for productivity. But in practice, it creates a perfect storm of hidden technical debt.
AI-generated code, while syntactically correct, lacks architectural intent. It doesn’t internalize domain models, adhere to established patterns, or respect long-term maintainability concerns. The result? A proliferation of brittle, copy-pasted logic. According to GitClear, AI-assisted commits have 8x higher duplication rates than human-written code. The result is a direct assault on foundational principles like DRY (Don’t Repeat Yourself), as well as increased complexity, fragility, and reduced readability.
What makes this worse is that the pain arrives sooner than ever. In traditional software lifecycles, legacy challenges might emerge after years of iterative development. But in the era of vibe coding, they show up in months. As business requirements evolve and new features are added, the lack of cohesive structure and deeply entangled code paths means debugging becomes exponentially harder. What was gained in velocity is now lost in resilience.
This creates a counterintuitive truth:
AI is accelerating our ability to create legacy code — not eliminate it.
Organizations betting on AI-assisted development without strong architectural guardrails are building systems that may work today but will become tomorrow’s legacy nightmares. Without intentional scaffolding, modular design, and rigorous testing, the velocity of AI turns from asset to liability.
A Call for More Sophisticated Modernization
This doesn’t mean we should shy away from AI in software development. On the contrary — AI is a powerful accelerator. But it must be harnessed responsibly. What we need are next-generation modernization pipelines that can deal with legacy being born faster than ever before. That includes:
- Architectural linting and AI-aware code review tools
- Continuous refactoring and DRY enforcement systems
- Modernization strategies that kick in during development, not years after
- Tooling that understands both AI and human-generated code and harmonizes them
In short, AI isn’t eliminating the legacy problem — it’s rewriting the timeline. What once took years to decay, now happens in quarters. To keep up, we’ll need not just faster AI tools — but smarter systems, better abstractions, and a renewed commitment to code quality in the age of automation.
Key Technical Barriers in General-Purpose Repository Translation
Despite the promise of LLMs for automating software modernization, a number of critical technical hurdles continue to obstruct reliable, scalable translation at the repository level. These challenges are deeply structural, not merely artifacts of model scale or token limits. Successful translation requires more than generating syntactically correct code — it demands consistent architectural transformation across an interconnected system of files, configurations, and dependencies. Below, we examine the key technical barriers in depth:
Deep Interdependency Challenges
One of the most significant barriers to repository-level translation is managing the dense web of interdependencies across files, modules, and layers of abstraction.
This is akin to a novice developer who can write clean individual functions but fails to grasp how the components integrate to form a coherent system. Without the ability to accurately trace dependencies and refactor function calls or shared types across multiple files, general-purpose LLMs frequently break repository integrity.
Configuration and Build System Complexity
Even when source code is accurately translated, projects often fail to compile or run due to misaligned configuration files and broken build systems.
Translation isn’t complete until the system builds and passes tests — and configuration files like pom.xml, build.gradle, tsconfig.json, or Makefiles play a critical role in defining dependencies, compile targets, and build steps. Unfortunately, LLMs struggle to parse the semantic intent encoded in these files, particularly when multiple environments, custom scripts, or conditional logic are involved.
Across multiple migrations I’ve led, from enterprise .NET solutions to modern microservice stacks, the build system often emerged as the most fragile element. Configurations had to be manually rewritten or adapted using custom transformers — something LLMs cannot yet generalize effectively across environments.
Deteriorating Performance with Complexity
Perhaps the most telling indicator of current limitations is how LLM performance sharply degrades with increased task complexity.
In practical terms, this means that while an LLM may successfully translate a small utility file or isolated class, it becomes significantly less reliable when transforming services with interdependent modules, shared types, or layered abstractions. I’ve seen accuracy drop from 90% for standalone scripts to under 40% when the LLM must translate multiple services while preserving system cohesion and behavior.
The implication is clear: LLMs are currently optimized for local context, not global coherence — a fatal flaw for repository-wide modernization.
Context Utilization Failures
A repository is more than the sum of its parts — its behavior is shaped by years of interrelated changes, domain logic, and architectural patterns. However, most LLMs still fail to meaningfully leverage this context.
The aiXcoder-7B-v2 research [3] demonstrates:
“Even when contexts contain useful information (e.g., relevant APIs or similar code), LLMs may fail to utilize this information effectively.”
This limitation is partly architectural. Transformer-based models can ingest large inputs, but struggle with selectively attending to the most relevant details in long-context scenarios. The issue isn’t just the size of the context — it’s the inability to reason across that context.
For repository translation, this often results in misaligned assumptions. For example, a model may reimplement an existing utility rather than reusing it, or make changes that break serialization rules established elsewhere in the code. Without awareness of naming conventions, design patterns, or existing abstraction layers, the model generates plausible code that subtly breaks the system.
This failure to internalize and reason over rich, structured context explains why human developers — who can reference the right part of the codebase and apply domain insight — still outperform LLMs in repository tasks by nearly 2x, as shown in the SWE-Lancer benchmark.
Structural, Not Superficial
These challenges aren’t just speed bumps in the road to automation — they are foundational gaps in LLM architecture and code reasoning capabilities. Dependency parsing, build system semantics, and context utilization all require system-level understanding, not token-level prediction.
For organizations looking to modernize legacy code, the lesson is clear: general-purpose LLMs cannot solve repository-level translation alone. Instead, success demands hybrid systems — tailored to each language pair, grounded in static analysis, guided by custom transformation logic, and supported by developer-in-the-loop workflows. Until foundational LLM capabilities evolve further, these barriers will remain critical bottlenecks for large-scale code modernization.
Why Traditional Approaches Will Fail
Many organizations are experimentally deploying general-purpose LLMs for code translation tasks. This approach will prove insufficient for three key reasons:
Context Window Limitations: Fragmented Understanding in Large Codebases
Despite recent advancements in large language models offering extended context windows — some reaching 128K tokens — these capabilities still fall short when applied to real-world enterprise repositories. Most production-grade codebases, particularly those that have evolved over years or decades, exceed these context limits by an order of magnitude. This leads to a fundamental problem: the model must operate on arbitrarily segmented slices of the code, severing logical and functional dependencies across files and modules.
As Wang et al.’s RepoTransBench [9] observes:
“LLMs with limited parameters may not have enough abilities to perform repository-level code translation.”
This limitation is akin to attempting to comprehend a novel by reading scattered paragraphs from different chapters with no knowledge of character arcs, plot evolution, or thematic consistency. It undermines the ability of the model to form a coherent understanding of the system as a whole.
This tendency to focus on proximal text causes models to overlook critical information found elsewhere in the repository — such as shared utility functions, global configuration settings, or class hierarchies. As a result, the accuracy and reliability of the model’s output suffer significantly.
To mitigate this, advanced migration workflows increasingly rely on semantic crawlers or static analyzers to traverse the repository beforehand. These tools extract relevant context (e.g., cross-references, type declarations, API usage) and package it into digestible, focused translation units — effectively simulating a broader context window. Without such augmentation, even the most powerful LLMs remain constrained in their ability to perform meaningful full-repository translations.
Build System Complexities: The Hidden Landmines of Modernization
While translating source code is challenging, ensuring the resulting code compiles, links, and executes correctly is often far more complex — particularly due to the intricacies of enterprise build systems. Tools like Maven, Gradle, Bazel, and Makefiles encapsulate build logic, dependency graphs, platform-specific configurations, and environment assumptions that general-purpose LLMs consistently misinterpret or omit.
Translating the code without properly reconstructing its corresponding configuration artifacts is like translating a recipe but omitting cooking temperatures, ingredient measurements, or preparation techniques. The resulting system is non-executable — a façade of functionality with no working backbone.
Executable environments often include custom scripts, environment variables, platform constraints, and undocumented assumptions that govern the software’s lifecycle. These components are not peripheral — they are central to system integrity.
From hands-on experience, the solution lies in treating build configurations as first-class translation targets. This means developing dedicated translation layers specifically for build artifacts, complete with schema validation, environment detection, and platform-aware mapping. Unfortunately, such layers are deeply platform-specific and do not generalize across ecosystems — a Kotlin-to-Swift pipeline cannot simply be reused for Java-to-Python. This necessity for bespoke tooling raises the engineering complexity of any large-scale codebase modernization effort.
Language-Specific Optimization Needs: The Limits of One-Size-Fits-All AI
Programming languages are not interchangeable ciphers — they encode different philosophies, performance assumptions, and safety constraints. Translating between them is not just a syntactic transformation; it requires deep awareness of each language’s operational semantics, runtime constraints, and idiomatic patterns.
This means that even small inefficiencies in translation can have real-world monetary or security consequences. Translating Solidity is less like translating prose and more like designing microcode — where resource constraints and state permanence impose strict architectural boundaries.
Similarly, moving from a statically typed language like Java to a dynamically typed one like Python (or vice versa) introduces mismatches in type checking, memory management, and exception handling. Languages differ in how they manage concurrency (e.g., async/await in JavaScript vs. goroutines in Go), how they structure object hierarchies, or how they resolve imports and namespaces.
These nuances demand language-pair-specific translation strategies — handcrafted modules that understand both the source and target paradigms in depth. Crucially, these strategies are not transferable. A solution built to translate between TypeScript and Rust will be largely useless when applied to C++ and Java. Thus, any serious modernization pipeline must account for the non-generalizable nature of real-world language translation — a fact that fundamentally limits the utility of general-purpose LLMs.
What to Expect in the Next 24–36 Months
The next three years will be pivotal for the evolution of automated repository-level code translation. Advancements across multiple dimensions — from specialized tooling to architectural frameworks — are poised to dramatically reshape the modernization landscape. Below are five critical developments likely to define the near future of this field.
Specialized Language-Pair Translation Tools
The emergence of high-performance tools tailored to specific language pairs — such as Java-to-C# or Python-to-JavaScript — will mark a significant breakthrough. These systems will be designed with deep awareness of syntax, semantics, and ecosystem-level compatibility. Based on recent progress like Zhang et al.’s Skeleton-Guided Translation, which aligns structural mappings between source and target languages, we’re likely to see tools consistently achieving 70–80% automation rates — far surpassing general-purpose LLMs.
This is analogous to how machine translation systems excel at well-understood language pairs like English–Spanish, but stumble with less commonly aligned pairs. Similarly, aiXcoder-7B-v2 [3] demonstrated that applying reinforcement learning (e.g., CoLT) to fine-tune models for specific translation tasks significantly boosts performance. Drawing from firsthand experience leading cross-language migrations, the greatest automation gains are likely to be found in pairs with shared programming paradigms — such as object-oriented or event-driven architectures — where structural similarities ease mapping across type systems, memory models, and idioms.
Incremental and Progressive Translation Frameworks
Full repository translation will shift from monolithic batch conversion to progressive, incremental systems capable of updating only the modified components while preserving module boundaries and interface contracts. This mirrors how software updates today often patch only affected components without reinstalling the entire application.
The future lies in progressive migration toolchains that support co-existence between legacy and translated code. These systems will enable safe, incremental evolution where AI-translated modules are tested, integrated, and validated alongside their original counterparts — minimizing risk while accelerating modernization timelines.
Domain-Specific Translation and Reasoning Systems
Generic language translation tools will give way to domain-specialized translation engines equipped with contextual knowledge of the application area — whether that’s financial modeling, statistical computing, scientific simulations, or blockchain development.
Peng et al.’s Solidity evaluation [11] showed how even the best-performing LLMs can fail at relatively simple logic in highly specialized domains:
“While LLMs can generate pretty nice contracts with challenging requirements, they can fail in some really easy cases.”
This reinforces the need for domain-adaptive training, where translation engines learn to reason with domain-specific constraints — such as gas fees and immutability in Solidity, or vectorized operations in NumPy-heavy Python.
aiXcoder-7B-v2 [3] found that domain-specific reasoning improves when models overcome their inherent bias toward nearby context and instead learn to integrate long-range dependencies. In practice, this means better performance on tasks like scientific computing or ML pipeline translation, where critical context may reside across modules or embedded in long sequences of transformation logic.
Build System and Environment-Aware Models
One of the most persistent bottlenecks in repository-level translation is the handling of build systems, configuration files, and deployment environments. These aren’t auxiliary concerns — they are often the difference between a working system and one that silently fails to execute.
Expect to see the rise of build-aware translation layers, trained not only on source code, but also on project scaffolding artifacts like pom.xml, package.json, .bazelrc, and CI/CD scripts. These systems will embed schema-aware logic to understand dependency resolution, plugin loading, and conditional compilation — allowing seamless transitions across ecosystems like Maven to Gradle, or Docker Compose to Kubernetes.
Such capability will become essential for enterprise migration, where “translating the code” is only half the battle — the other half is ensuring that it compiles, integrates, and runs as expected within its deployment environment.
Maturing Model Context Protocol (MCP) Ecosystem
The Model Context Protocol (MCP) is on the path to becoming a core standard for managing AI-to-tool interoperability at scale. As of now, its ecosystem includes notable players like OpenAI, Cursor, Copilot, Cloudflare, and Windsurf, but adoption is poised to expand significantly.
This shift will usher in an era of secure, modular AI orchestration, where LLMs can fluidly collaborate with syntax checkers, linters, test runners, and deployment verifiers — all through a common interface. However, with this growth comes increased exposure: MCP will also need to address security hardening
A robust MCP layer will become the nerve center of AI-powered modernization pipelines, ensuring that LLMs can operate safely, securely, and contextually within enterprise repositories.
The Economic Imperative Behind Legacy Modernization
Repository-level code translation is poised to become a transformative force in legacy modernization — but not through the one-size-fits-all, general-purpose LLMs that dominate today’s hype cycle. The true breakthrough lies in purpose-built systems that integrate LLM strengths with traditional compiler logic and deep domain expertise. Organizations that pursue this hybrid path will unlock significant competitive advantages, accelerating transformation without sacrificing stability or precision.
Legacy code is not just a technical artifact — it’s a strategic liability or asset, depending on how it’s handled. The core question isn’t whether your codebase is pristine, but whether it can evolve fast enough to meet the pace of market change. As Peter Gabriel of ThoughtWorks framed it with his idea of “habitable code”, it’s not about making the whole house immaculate — it’s about keeping the high-traffic areas functional and clean. This mindset echoes best practices from Six Sigma, which focuses on high-impact process improvement, and the 5W1H framework, which targets clarity through structured questioning.
The most forward-thinking organizations are those building tailored, domain-aware translation systems — solutions designed specifically for their architecture, language stack, and development workflows. They understand that waiting for generic, universal LLMs to become production-ready is not a strategy; it’s a stall. Instead, they act now — creating infrastructure and processes that deliver tangible modernization gains today.
Ultimately, the future of modernization lies not in abandoning traditional methods, nor in blindly embracing every new AI capability. It lies in deliberate orchestration — a thoughtful convergence of compiler rigor, human judgment, and LLM acceleration. By putting AI under control, organizations can turn the daunting complexity of legacy systems into a series of focused, solvable challenges — and reclaim velocity without compromise.
