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Before rewriting a legacy system, use AI to help explain what it does, then verify those explanations against code, tests, documentation, runtime behavior, and business knowledge. Modernization should begin with discovery, not with an assumption that replacement is the answer. A grounded understanding of existing behavior helps teams choose among refactoring, migration, replacement, or a rewrite—and identify what must keep working through the transition.
Why understand a legacy system before changing it?
A system’s code is only part of its design. Its behavior may also reflect undocumented business rules, dependencies on other applications, data conventions, operational practices, and decisions made long ago for reasons that are no longer obvious. If a rewrite overlooks one of these, the new system can be cleaner yet fail to perform a necessary job.
That is why understanding the existing implementation is itself a modernization activity. IBM’s modernization guidance begins with understanding the codebase, including documentation review, static analysis, and dependency visualization. The UK Department for Environment, Food & Rural Affairs (Defra) similarly begins its application modernization playbook by understanding the application and assessing technical and business risks.
AI can make discovery more tractable when a codebase is large or the people who know it best are unavailable. It can explain a bounded section of code, surface candidate rules, and help organize questions for investigation. But an explanation is a hypothesis about the system, not proof that the system behaves as described. The practical goal is not to have AI pronounce whether a system should be rewritten; it is to assemble enough checked evidence for people to make that decision.
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What can AI help you learn from legacy code?
AI can help translate implementation details into explanations and candidate requirements, especially when its analysis is grounded in the relationships between parts of the code. It is most useful as an assistant for exploration, not as an authority on undocumented intent.
Explain bounded parts of the implementation
Ask about a specific module, function, data transformation, or path through the application rather than requesting a confident account of an entire unfamiliar system at once. A useful explanation identifies inputs, outputs, conditions, side effects, and dependencies, and points back to the evidence used. That gives a reviewer something concrete to check.
In “Legacy Modernization meets GenAI,” published on 24 September 2024, Thoughtworks authors Alessio Ferri, Tom Coggrave, and Shodhan Sheth describe combining a large language model with a knowledge graph derived from code syntax trees. The approach uses code relationships to help focus analysis and generate low-level requirements and higher-level explanations. The lesson is broader than any particular tool: code context and relationships matter. An answer that ignores callers, data flow, or related modules can sound plausible while missing what the code actually means in the application.
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Code can reveal what a system does under particular conditions, even when a formal requirement is missing. AI can help express those observations as candidate requirements—for example, which conditions lead to a branch, what data is changed, or what outcome is returned. Keep the word candidate in view: implementation behavior does not by itself explain why a rule exists or whether it remains a business requirement.
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Preserve the connection between each claim and its evidence. A useful record identifies the relevant code location or other source, the observed behavior, unresolved questions, and who or what can verify it. Without that traceability, a fluent explanation can become detached from the code and be repeated as fact.
Organize investigation, not just summaries
Use AI to help list unknowns, group related areas, and identify questions that need answers from application owners or business specialists. This can be valuable when no single person has a complete view. It does not fill the knowledge gap automatically: where evidence is missing or experts are unavailable, record the uncertainty instead of allowing an AI-generated guess to stand in for an established rule.
How should you use AI to discover what the system does?
Work from evidence to explanation, then from explanation to a testable account of behavior. A staged discovery process keeps the system’s actual implementation and dependencies in view while making the work reviewable.
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- Map the system’s shape. Identify modules, dependencies, interfaces, data movement, and important branches. Static analysis and dependency visualization can help expose structure; runtime inspection can help check what happens when the application executes. Keep a distinction between a relationship visible in code and a dependency confirmed in operation.
- Ask narrow, evidence-oriented questions. Give AI a bounded area and enough surrounding context to interpret it. Ask it to explain the path, conditions, data changes, and dependencies, and to identify what it cannot establish from the supplied evidence. Ask for candidate requirements rather than an unqualified statement of business intent.
- Keep a traceable explanation. For each useful claim, record where it came from, whether it is supported by code, documentation, a test, runtime observation, or a knowledgeable person, and what remains uncertain. This makes review and later correction possible.
- Check the explanation against behavior. Compare it with existing documentation and tests, inspect relevant runtime behavior, and ask people familiar with the business process to review the implications. Where appropriate, create characterization tests to capture what the current code actually does.
- Assess risk before selecting a change. Consider technical and business risk, dependencies, compatibility, evidence about behavior, and the needs of transition. Define the target architecture before treating a build plan as a decision about what to replace.
- Change, test, and transition deliberately. Where the architecture and risks permit, make changes incrementally, test the resulting behavior, deploy and transition the work into support, and record lessons. Defra’s playbook includes these stages after understanding, risk assessment, and target architecture.
How do you verify an AI explanation?
Do not accept an explanation because it is detailed, well-written, or confident. Check each material claim against evidence that can establish the relevant behavior, and keep unresolved questions visible.
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- Check code paths and relationships. Confirm that the explanation covers relevant callers, conditions, data flow, and effects—not just the isolated lines supplied to the model.
- Compare with existing evidence. Look for agreement or conflict with requirements, comments, change history, tests, and operational documentation. A conflict is a question to investigate, not a reason to choose whichever source sounds clearer.
- Observe the application where useful. Runtime inspection can help test how behavior occurs in execution. It complements code analysis; it does not automatically explain why the behavior exists or whether it is still wanted.
- Ask people to review business meaning. Someone familiar with the relevant process may recognize an essential rule, an exception, or an outdated behavior that code alone cannot explain.
- Test before shipping a change. Microsoft Learn warns that “AI-generated plans and code can contain errors.” Its Windows desktop modernization planning guidance tells users to verify recommendations against linked documentation, review generated changes, and test the app before shipping. The verification principle is useful beyond Windows, but the page’s migration instructions are specific to that context.
For consequential systems, the supervision bar should be higher. In its 5 June 2025 article “Legacy IT Modernization with AI,” MITRE authors Nitin Naik and Justin Brunelle report that measured model performance did not match subject-matter experts’ quality perceptions. They recommend that LLMs be used “in a highly supervised manner” for legacy modernization of mission-critical systems. Their investigation concerns federal, mission-critical legacy systems; they also note that performance for the distinctive combination of legacy IT languages and complex government systems remains unproven. Treat this as a reason for careful oversight, not as a numerical estimate of how often AI will be wrong.
Should you rewrite, refactor, migrate, or replace the system?
Choose an approach after discovery and risk assessment. These options are not mutually exclusive: IBM describes refactoring, migration, and rewriting as approaches that can be combined, while a rewrite creates a new system and can be a substantial undertaking.
| Approach | What it means | Questions to resolve first |
|---|---|---|
| Refactoring | Change the implementation while continuing to work with the existing system. | Which behaviors must remain stable? Can tests and other evidence give confidence that the internal changes preserve them? |
| Migration | Move the system or parts of it to a different platform or environment. | Will required behavior and interfaces remain compatible with the target platform? What dependencies and transition needs must be handled? |
| Replacement or rewriting | Build a new system to take over some or all of the existing system’s role. | Which business rules and behaviors must the new system preserve? How will old and new behavior be compared, and how will transition and coexistence work? |
The labels alone do not establish cost, duration, or risk. Those depend on the application, its dependencies, the target architecture, and the evidence available about behavior. In particular, a rewrite should not be treated as the default merely because the current code is difficult to read. If discovery exposes a smaller, safer change that meets the need, that may be a better fit; if it exposes constraints the current architecture cannot meet, the case for a larger change can be evaluated on evidence.
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What should you compare before choosing an approach?
Compare candidate approaches against the same practical criteria, rather than judging them only by how modern the resulting code or platform would be. The following framework synthesizes the risks and sequencing described by IBM and Defra; it is a way to structure a decision, not a universal scoring formula.
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- Behavior and business rules: What observed behavior must continue, and which rules have been confirmed as current requirements rather than merely inferred from implementation?
- Technical and business risk: What could fail in the system or the business process if a dependency, exception, or operational assumption is misunderstood?
- Target-platform compatibility: Does the proposed destination support the application’s necessary behavior and constraints?
- Cross-system dependencies: Which interfaces, data flows, or other applications rely on the existing system, and how would each option affect them?
- Test and comparison evidence: Can the team test important behavior and compare the old and changed systems? Where coverage is weak, what characterization tests or other evidence can reduce uncertainty?
- Transitional coexistence: Would old and new components need to run together, and what complexity would that introduce?
- Staged delivery and learning: Can the work be divided into changes that can be tested and learned from, or does the architecture or transition require a larger coordinated change?
Make uncertainties part of the decision record. If an approach depends on a business rule no one has confirmed, a dependency that has not been mapped, or behavior that cannot yet be tested, state that openly. Those are actionable discovery needs, not details to hide inside an AI-generated plan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the main pitfalls when using AI on old systems?
The main failure mode is confusing a plausible account of the code with a verified account of the system. Avoid these common shortcuts:
- Asking for a whole-system verdict too early. Broad prompts can encourage an answer that glosses over modules, exceptions, or dependencies. Break investigation into reviewable areas and preserve how they connect.
- Treating implementation as intent. Code demonstrates behavior in particular paths; it does not alone prove that the behavior is still required or explain its business purpose. Seek confirmation from requirements, operational evidence, or knowledgeable people.
- Letting generated text lose its provenance. If an explanation is copied without its code context, evidence, and uncertainty, future readers may mistake it for a verified specification. Keep those together.
- Skipping behavior tests because the explanation sounds right. Use characterization tests where possible to record current behavior, then test refactored, migrated, or rewritten code. IBM describes characterization tests as a way to help teams comprehend what code does.
- Assuming AI resolves a lack of expertise. AI can help surface questions, but cannot establish missing business context just by generating an answer. Escalate decisions whose consequences matter and whose evidence remains incomplete.
- Turning an experiment into a universal claim. Thoughtworks reports practitioner experience with its internal CodeConcise accelerator, not a general benchmark or proof that every tool or codebase will see the same results. Its article supports the value of investigating existing code; it does not establish a guaranteed productivity outcome.
When is a rewrite justified?
A rewrite becomes a defensible option when discovery and risk assessment show why other approaches do not adequately meet the need, and the team can explain how necessary behavior and dependencies will be handled. The decision should follow a target architecture and a credible plan for testing and transition, not precede them.
Evidence for a decision can include a map of dependencies and interfaces, a reviewed account of important behavior, tests that capture critical existing behavior, identified technical and business risks, and a comparison of candidate approaches against compatibility and transition needs. The amount and form of evidence will vary by system; the important point is that the choice is grounded in the system and its use rather than in a general preference for new code.
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When the evidence supports a larger change, the same discovery work still matters. It identifies what the new system must do, which integrations need attention, what behavior needs comparison, and where uncertainty remains. A rewrite replaces implementation; it does not erase the obligations created by users, business rules, data, and connected systems.
Frequently Asked Questions
What is a characterization test in legacy modernization?
It is a test used to capture what existing code does, including behavior that may not have been documented as a formal requirement. Its value is as a record of observed behavior for understanding and comparison, not as automatic proof that every recorded behavior is desirable.
Can AI discover business rules that are missing from the documentation?
It can help identify candidate rules by analyzing code paths and expressing their conditions and outcomes. Whether a candidate reflects current business intent still needs confirmation from appropriate evidence or people familiar with the process.
What if no one on the team understands the legacy application?
Start by inventorying available artifacts and recording unanswered questions rather than inventing explanations. Code analysis, static structure, runtime observations, and tests can build a partial account, but high-impact unknowns still need explicit ownership and supervised review.
Does AI make a legacy rewrite faster or cheaper?
The cited sources do not establish a general time or cost saving. Thoughtworks describes practitioner experience with an internal accelerator, while MITRE reports that model measures did not align with expert quality perceptions in its investigation; neither supports a universal productivity estimate.
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