Moderne Unifies Frontend and Backend Code Upgrades With Type-Attributed JavaScript Refactoring

/ AI, Application Development, DevSecOps
Moderne Unifies Frontend and Backend Code Upgrades With Type-Attributed JavaScript Refactoring

The News

Moderne has expanded its enterprise code modernization platform to include type-attributed JavaScript and TypeScript refactoring, extending its Lossless Semantic Tree (LST) model across both backend and frontend codebases. This makes Moderne the first deterministic modernization platform that is capable of analyzing and refactoring Java, JavaScript, TypeScript, and Infrastructure-as-Code (IaC) with full type attribution and data-flow control.

Analysis

Modernization Confronts a Multilingual Reality

Moderne’s expansion comes as enterprises face growing pressure to modernize heterogeneous application estates spanning Java, JavaScript, and hybrid infrastructures. For years, modernization efforts have been language-siloed, with distinct tools for backend and frontend refactoring, each with inconsistent safety guarantees. Moderne’s Lossless Semantic Tree (LST) aims to bridge that divide by providing a compiler-accurate model of code semantics that may allow developers to perform cross-language modernization at scale.

This timing is strategic. theCUBE Research and ECI Day 0 data shows 76% of developers are already highly familiar with cloud-native principles, but 24% cite complexity and skill gaps as barriers to automation. By providing deterministic, type-safe automation, Moderne could address a critical market need for consistent modernization pipelines that can evolve as quickly as AI-generated code expands the software footprint.

AI-Driven Code Growth Spurs Modernization Urgency

The rise of AI-assisted development has amplified modernization demand and complexity. Industry data cited in the release illustrate an increase in new, AI-generated code (up to 46% of total commits) leading to increased technical debt and maintenance risk. theCUBE Research and ECI similarly reports that 70.4% of enterprises rank AI/ML tools among their top spending priorities, yet 62.7% name security and compliance as equal concerns.

Moderne’s deterministic approach aims to provide enterprises with a safety layer for AI-accelerated coding environments, which could ensure that the output of human and machine contributors can be refactored, validated, and governed through the same semantic lens. For developers, this could mean that AI adoption doesn’t have to introduce fragmentation or manual cleanup cycles. Instead, modernization becomes an embedded, ongoing process.

Addressing Modernization’s Hidden Risk of Security Debt

Security remains one of the most pervasive modernization challenges. In theCUBE Research and ECI’s DevSecOps study, 47.2% of respondents report incidents linked to cloud-native application vulnerabilities, while 50.9% identify APIs as the most susceptible layer. Moderne’s type-attributed analysis may enable teams to trace untrusted inputs, detect dependency risks, and automatically remediate vulnerabilities across JavaScript dependency chains. This capability was previously limited to backend environments.

By unifying security analysis and modernization under one platform, Moderne may shift security from reactive patching to proactive code-level defense, potentially turning modernization into a continuous assurance cycle rather than a one-time migration effort.

A Deterministic Platform for Enterprise-Scale Change

Unlike traditional codemods or syntax-bound AST tools, Moderne’s LST architecture preserves semantic fidelity across languages, allowing automated upgrades to integrate directly into CI/CD pipelines. Our Day 1 data shows 51.2% of teams fully automate infrastructure provisioning, but only 43.6% mostly automate deployments, highlighting the gap between automation intent and implementation. Moderne’s deterministic recipes could help close that gap by giving developers predictable, reproducible modernization workflows that fit naturally within DevSecOps practices.

By making modernization both safe and automatable, the goal of Moderne’s approach is to reduce the cognitive load on engineering teams and align with enterprise demands for repeatable, auditable change at scale, which is a key theme across modernization and compliance initiatives in 2025.

Looking Ahead

Moderne’s addition of JavaScript and TypeScript marks an expansion of the modernization landscape. It positions the platform as a cross-language control plane for refactoring, compliance, and AI code management, bridging the historical divide between backend reliability and frontend agility.

The next evolution of modernization is not about migrating code, but rather about operationalizing change itself.  Moderne’s deterministic, semantic approach could help enterprises do just that by turning modernization from a reactive, project-based exercise into an ongoing, measurable capability that spans every layer of the stack.

If Moderne continues to expand its LST coverage and integration with CI/CD and AI-native development tools, it may emerge as a main enabler of intelligent, governed modernization pipelines, a critical foundation for enterprises navigating the next decade of AI-accelerated software evolution.

Author

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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