Code Refactoring and Legacy Modernization Tools Compared

Until recently, modernizing a legacy system meant expensive consultants, months of reverse engineering work that could still leave critical gaps, and a high chance of breaking something nobody expected. AI has changed what is possible, but it has also produced a crowded, confusing market.

We’ve curated a comparison of ten tools in this space, namely

• Interlace — a graph-based AI platform that builds a live map of your codebase and uses it to drive AI agents through the full modernization process
• Phase Change Software — extracts business logic from COBOL systems using deterministic analysis, meaning every relationship in the code is traced directly from the source rather than guessed by an AI model.
• Swimm — maps legacy application behavior into structured, deterministic output that both AI agents and business analysts can use
• Blitzy — generates production-ready code at scale, covering everything from legacy migrations to new product builds.
• CAST — reverse-engineers applications into structural maps and provides the architectural context AI agents need to work on existing systems
• Devin — an autonomous AI software engineer that handles full development tasks end-to-end
• Factory AI — automates the full SDLC automation with specialized agents across any IDE or interface
• Thoughtworks CodeConcise — turns legacy code into verified business logic specifications. Its not a standalone product and is only available via a consulting engagement with Thoughtworks
• Sourcegraph — code understanding platform for developers and AI agents across large codebases
• CodeScene — pinpoints the technical debt that is actually slowing your team down by combining code quality metrics with data on how the codebase is being worked on

The transcript below takes a closer look at each tool.

Interlace

Interlace is a graph-based AI engineering platform built by AltexSoft. It reverse-engineers legacy codebases and builds a live, queryable knowledge graph that captures code structure, business workflows, user flows, and cross-service dependencies. AI agents can then use this graph as a source of truth during modernization.

Capabilities

• Reverse-engineers multi-service, multi-repo codebases in any language into a continuously running knowledge graph
• Captures business logic, user flows, domain rules, and cross-module dependencies
• Exposes the graph via MCP so AI agents in Cursor, Copilot, or Claude work with a verified system context

AI use case. Multi-agent planning, code generation, refactoring, and consistency checks, all grounded in the verified system graph.

Automated code/test generation. Yes. Modules, refactors, glue code, and tests generated via agents.

Depth of legacy modernization. High. Interlace builds a verified system graph and performs end-to-end modernization and feature development, not just analysis.

Business logic capture. Yes. Reverse-engineers user flows, domain rules, and cross-module business logic across any language.

Main focus. Modernizing and extending complex legacy systems with AI agents grounded in a live, verified system graph.

Phase Change Software

Phase Change Software extracts what COBOL systems actually do for the business. Where most tools stop at code structure, Phase Change goes further to extract the rules and decisions behind it. It uses symbolic AI to trace every business function directly from the source code, producing named, traceable outputs tied to specific modules. A language model is then used only to translate those findings into plain language; it plays no role in the analysis itself.

Capabilities

• Deterministic extraction of business functions, rules, calculations, and decision logic from COBOL source code
• Produces named business functions with identified inputs, outputs, and confidence scores
• Natural language interface for querying the knowledge graph
• The tool runs on-premise, so source code never leaves the client environment.

AI use case. Uses symbolic AI to extract business logic from the COBOL code, with a language model brought in only to translate those findings into plain language.

Automated code/test generation. No. Phase Change produces understanding and documentation only.

Depth of legacy modernization. Medium. The tool explains what COBOL systems do and enables informed modernization decisions, but does not execute changes.

Business logic capture. Yes. Deterministically extracts business functions from COBOL source code.

Main focus. Understanding and documenting what COBOL systems do for the business before replacement or migration.

Swimm

Swimm is an application understanding platform that produces a deterministic, accurate picture of legacy application behavior, which then becomes a foundation for AI-assisted modernization. It analyzes source code structure and traces business rules, decision paths, and data flows directly from it. The platform feeds structured context to AI agents via MCP and keeps that picture updated as code changes.

Capabilities

• Deterministic extraction of business rules, decision paths, and data flows from source code
• Supports COBOL, JCL, PL/I, and modern legacy languages
• Exposes structured context to AI agents via MCP
• IDE plugin for documentation and codebase Q&A
• On-premise deployment with customer-managed LLMs; SOC 2 and ISO 27001 compliant

AI use case.  Uses AI only to convert the analysis output into human-readable language; the analysis and extraction are done entirely through deterministic methods.

Automated code/test generation. No. Understanding and documentation only.

Depth of legacy modernization. Medium to high. Swimm reports a 90 percent reduction in manual reverse engineering time and 100 percent business rule extraction. It provides the understanding layer but does not execute changes.

Business logic capture. Yes. Swimm extracts business rules, decision paths, and data flows directly from the source code, primarily from COBOL and mainframe systems.

Main focus. Providing verified application understanding as the foundation for AI modernization programs.

Blitzy

Blitzy is an autonomous, language-agnostic code-generation platform that ingests a full codebase, builds a knowledge graph of its structure, and orchestrates thousands of AI agents to generate production-ready code. It covers the full range of development work from new products and migrations through to testing and documentation.

Capabilities

• Ingests codebases of 100M+ lines in a single pass
• Orchestrates 3,000+ AI agents that plan, build, and validate code
• Generates up to 3M lines per run, validated at compile and runtime
• Produces a developer guide for the remaining 20 percent of work requiring human judgment

AI use case. Large-scale autonomous code generation across the SDLC.

Automated code/test generation. Yes. Blitzy generates code—including tests—autonomously in large batches and validates every output at compile and runtime before delivery.

Depth of legacy modernization. High. Btlizy handles complex migrations, including COBOL-to-modern language rewrites, and can generate up to 3 million lines of code per run.

Business logic capture. Partial. The knowledge graph captures code structure and dependencies, not business meaning or intent.

Main focus. Autonomously generating large volumes of enterprise-grade code across new products, refactors, and migrations.

CAST

CAST provides software intelligence across application portfolios through two main products that both feed verified, deterministic architectural context to AI agents via an MCP server. CAST Imaging deep-analyzes individual applications by reverse-engineering source code into a structural knowledge graph; CAST Highlight gives executives and architects portfolio-level views of technical debt, cloud readiness, and open-source risk.

Capabilities

• CAST Imaging reverse-engineers source code, DB scripts, and config files across 450+ technologies into a structural graph
• CAST Highlight scans the full application portfolio to surface technical debt, cloud migration blockers, and open-source risks at a glance.
• MCP server connects CAST's architectural context to AI agents

AI use case. Deterministic structural analysis that feeds verified architectural context to AI agents via MCP.

Automated code/test generation. No. CAST analyzes and maps, but doesn’t write or modify code.

Depth of legacy modernization. Medium to high. CAST maps architecture, flags risks, and sequences modernization priorities, but doesn’t execute changes.

Business logic capture. No. CAST maps what calls what and how systems are structured, with no insight into business meaning or intent.

Main focus. Portfolio-level architecture analysis, risk scoring, and providing deterministic context for AI-assisted modernization.

Devin

Devin is an autonomous AI software engineer that executes complete development tasks end-to-end, from reading a ticket to opening a pull request. It operates with a full embedded IDE, terminal, and browser, and can run multiple sessions in parallel. It maintains a knowledge base and repo index that persists across sessions, though it does not build a structural system graph.

Capabilities

• Full autonomous task execution that includes feature development, bug fixes, documentation, etc.
• Parallel sessions for large-scale work, where multiple instances run simultaneously across different modules
• Devin Review, a built-in code review tool, flags potential bugs and lets developers ask questions about the codebase directly within the review chatbot interface
• MCP server available for programmatic session management

AI use case. Full task execution covering planning, coding, testing, and debugging.

Automated code/test generation. Yes. Full autonomous generation across features, refactors, migrations, and tests.

Depth of legacy modernization. Medium. Devin performs well on well-scoped, repetitive migration tasks.

Business logic capture. No. Devin executes tasks based on instructions, but doesn’t extract or model business logic from the codebase.

Main focus. Delegating complete engineering tasks to an autonomous AI agent.

Factory AI

Factory AI automates the full software development lifecycle using specialized agents called Droids. It covers feature development, migrations, code review, testing, incident resolution, and documentation. The platform maps codebase dependencies before generating code and has published Legacy-Bench, a benchmark for measuring AI agent performance on COBOL, Fortran, Java 7, and other legacy languages.

Capabilities

• Maps codebase dependencies before generation begins to guarantee context-aware output
• Handles feature development, refactors, migrations, bug fixes, security fixes, testing, and documentation
• Maintains full test coverage throughout migrations

AI use case. Feature development, migrations, code review, testing, and incident resolution across the full development lifecycle.

Automated code/test generation. Yes. Full autonomous code generation with test coverage maintained during migrations.

Depth of legacy modernization. Medium to high. Factory claims migrations in weeks rather than months, with dependency mapping and full test coverage.

Business logic capture. No. Factory executes tasks but does not extract or model business logic from the codebase.

Main focus. Full SDLC automation with specialized agents across any tool, model, or interface.

Thoughtworks CodeConcise

Thoughtworks CodeConcise is a legacy modernization accelerator that reverse-engineers legacy codebases into verified business logic specifications using AST-based parsing and a knowledge graph. It is now part of AI/works, Thoughtworks' broader agentic development platform. CodeConcise is a standalone tool, but it is only available through a Thoughtworks consulting engagement.

Capabilities

• AST-based parsing breaks the code down into its meaningful building blocks like functions, classes, and methods, visualizes how they relate to each other, and maps dependencies across modules
• Generates documentation and machine-readable business logic specifications from legacy code
• Supports COBOL, C++, Python, and IDMS

AI use case. Code comprehension, business rule extraction, documentation generation, and reverse engineering acceleration.

Automated code/test generation. No. CodeConcise produces understanding and specifications only.

Depth of legacy modernization. High. Production-proven on a real client at scale. It specifically addresses the reverse engineering phase and doesn’t execute migrations or write code.

Business logic capture. Yes. Extracts business rules and functional specifications from legacy code by analyzing it in multiple passes, each one building a more complete picture of what the system does.

Main focus. Reverse-engineering legacy code into verified business logic specifications before migration begins.

Sourcegraph

Sourcegraph is a code understanding platform that gives developers and AI agents the context to search, navigate, and reason about large, complex codebases.

Capabilities

• Deep Search provides semantic, cross-repository, and historical answers to questions about the codebase
• Code Search enables fast, exhaustive literal, regex, and semantic search across 1M+ repositories
• Batch Changes automates large-scale code edits across all repositories simultaneously
• The Cody assistant provides AI code completions and chat in VS Code, JetBrains, and Visual Studio

AI use case. Semantic search and code understanding to provide accurate, cross-repository context for developers and AI agents.

Automated code/test generation. Partial. Batch Changes automates large-scale code edits across repositories; Cody provides AI code completions and edits.

Depth of legacy modernization. Low to medium. Sourcegraph provides deep code intelligence and can automate bulk changes, but it is not a modernization platform. It is most useful for understanding what exists before making changes.

Business logic capture. No. Sourcegraph maps code structure, search results, and repository history, but doesn’t extract business rules or intent.

Main focus. Code understanding and intelligence for both developers and AI agents across large, complex codebases.

CodeScene

CodeScene measures and manages technical debt by combining static code analysis with behavioral data from version control. Its CodeHealth metric identifies complex code areas and where that complexity is actively slowing the team down. There’s also CodeScene ACE, a companion tool that adds AI-powered refactoring directly in the IDE for fixing the issues it detects.

Capabilities

• CodeHealth metric aggregates 25+ factors from source code
• Hotspot analysis identifies files that are both frequently changed and low quality, prioritizing refactoring by business impact
• CodeScene ACE auto-refactors code smells in the IDE with minimal, targeted changes
• Acts as a quality gate for AI-generated code from Cursor and GitHub Copilot

AI use case. Risk scoring, change prediction, and AI-assisted refactoring of existing code; quality-gating of AI-generated code in real time.

Automated code/test generation. Partial. CodeScene ACE auto-refactors existing code smells, but doesn’t generate new features or tests.

Depth of legacy modernization. Low to medium. CodeScene identifies where to refactor and auto-refactors hotspots, but doesn’t execute migrations or rewrites.

Business logic capture. No. CodeScene maps code structure, complexity, and change behavior; it doesn’t extract business rules or intent.

Main focus. Identifying high-impact technical debt and code hotspots by combining code quality analysis with behavioral data from version control.