Automated Refactoring Suggestions

Technical debt is a significant and growing burden on technology organizations. Code written over the years still works in most cases, but it may be bloated, fragile or rigid and thus not as efficient as it can be. If all the world’s roughly 25 million software developers did nothing but address this technical debt it would take them until 2034, or 61 billion workdays, to fit it all, according to the report “Coding in the red: The State of Global Technical Debt 2025” from CAST, a provider of software intelligence services. This accumulation of suboptimal code slows development, increases maintenance costs, and elevates the risk of system failures.

The process of reworking code to make it more efficient, without changing what it does, is called refactoring. The manual refactoring process is time-consuming and often not a priority. It presents significant operational challenges for commerce organizations managing complex, interconnected systems that often interface with modern microservices architectures, creating layers of abstraction that further complicate refactoring efforts.

Automated refactoring solutions leverage advanced machine learning models and natural language processing to analyze codebases and suggest structural improvements. These tools analyze code to detect bad coding practices often called “anti-patterns”, suggest or implement improvements, and modernize outdated segments. Unlike traditional static analysis tools that flag issues based on hardcoded rules, AI-based systems learn from vast repositories of code, identifying deeper insights such as suboptimal naming conventions or overcomplicated logic, and often proposing the improved code directly.

The technical architecture combines multiple AI techniques. The refactoring engine builds comprehensive Abstract Syntax Tree (AST) representations, analyzes data flow, and identifies refactoring opportunities. These systems employ LLMs trained on millions of code examples to recognize patterns across different programming languages.

A critical component of this process is robust testing. Organizations need enhanced testing protocols specifically designed for AI-refactored code, including comprehensive regression testing to verify functional equivalence, performance testing to confirm optimization benefits, and security analysis to ensure refactoring has not introduced new vulnerabilities. The most effective approach combines automated testing with human oversight focused on business logic validation.

Despite significant capabilities, these systems face important limitations. Teams need training in how to effectively prompt AI models for refactoring tasks and evaluate AI-generated suggestions. The technology struggles with highly domain-specific business logic, particularly in commerce systems where complex pricing rules or inventory calculations may not be well-represented in training data. Organizations must establish clear boundaries for automated refactoring, focusing initially on well-understood patterns.

Leading commerce organizations have achieved measurable success implementing automated refactoring solutions. The global microservices architecture market was valued at $4.2 billion in 2024 and is projected to reach $13.1 billion by 2033, growing at a CAGR of 12.7% (IMARC, 2024). Automated refactoring tools are playing a crucial role in enabling these transformations.

A Fortune 500 retailer transformed a Java monolith into microservices architecture while maintaining 24/7 operations, according to a case study by Morph, a provider of coding agents and tools. The retailer extracted 40 microservices from more than 3 million lines of Java code, achieved a 75% reduction in coupled components, eliminated duplications that had amounted to 60% of the reworked code, and reduced deployment cycles from 12 hours to 30 minutes, Morph says.

Brazilian digital financial services company Nubank utilized AI tool Devin to rework some 6 million lines of code in its monolithic ETL (extract/transform/load) system into smaller, more flexible sub-modules, according to a Devin case study. Nubank had estimated the project would involve more than 1,000 engineers moving roughly 100,000 data class implementations over 18 months, a huge investment in scarce resources. Instead, Nubank taught the AI-powered Devin tool how to approach sub-tasks, enabling Devin to complete the migration autonomously, with a human kept in the loop just to manage the project and approve Devin’s changes. To improve performance, the Nubank team fed examples of previous manual migrations into Devin for fine-tuning, resulting in a doubling of Devin’s task completion scores as well as a 4x improvement in task speed. Overall, the Devin case study says, using AI increased engineering time efficiency by 8x and cost savings by 20x.

“AI is fundamentally reshaping software development by automating code generation, bug detection, testing, and even documentation,” Grand View Research said in a report projecting the size of the AI in software development market from 2025-2033. Grand View noted the latest version of Microsoft’s Visual Studio IntelliCode, announced in March 2025, delivers AI-powered code completion and automation within development workflows. “IntelliCode now offers whole-line autocompletions, context-aware recommendations, and intelligent refactoring, helping developers code with greater accuracy and efficiency,” the report says.

While refactoring is only one of many ways software developers are using AI, its growing adoption is part of the overall increase in spending for AI-powered tools to help develop and improve software. Grand View estimates that 313 3.4 Build spending would increase from $674.3 million in 2024 to reach $15.7 billion by 2033, a CAGR of 42.3% from 2025 to 2033.

The automated refactoring solution market encompasses diverse providers, from IDE-integrated code suggestions to enterprise-scale transformation platforms. The market segments into comprehensive development platforms, specialized refactoring tools, and AI-powered coding assistants.

Enterprise platforms designed for large-scale initiatives provide comprehensive capabilities for analyzing and transforming entire codebases. These solutions offer sophisticated dependency analysis, impact assessment, and rollback capabilities. Key considerations include support for legacy languages common in commerce platforms and the ability to handle complex architectural patterns.

The emergence of AI-powered coding assistants has democratized access to refactoring capabilities. These tools integrate directly into development environments, providing real-time suggestions. Selection criteria should include the accuracy of suggestions, the ability to learn from organization-specific patterns, and support for collaborative workflows.