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Before you refactor, you need a reliable baseline.
Legacy systems often hold years of undocumented business logic, hidden dependencies, and edge cases. Without sufficient test coverage, even a seemingly minor change can introduce unexpected regressions. Building that safety net manually can take months.
The bottleneck isn’t always the modernization itself. It’s making the code safe to modernize.
Why Legacy Modernization Stalls
Legacy doesn’t always mean obsolete. It often means mission-critical and difficult to change safely. Programs tend to fail in three ways:
- Scope creep: a seemingly simple function may encode years of exceptions, regulatory handling, and operational workarounds nobody documented.
- Regression risk: the new system only succeeds if it preserves the behavior the business depends on, and that behavior often isn’t obvious from the code.
- Capacity exhaustion: modernization needs the same senior engineers who keep the current system running. The bottleneck is often expert attention, not budget.
The Risk of Naive AI Modernization
AI can analyze code, generate tests, and refactor repeatable patterns at a speed humans can’t match. Used poorly, it’s a faster way to create AI-generated technical debt.
The dangerous version is code-first refactoring at scale: the output looks cleaner and more modern, but the key question isn’t whether the new code looks better. It’s whether the system still behaves the way the business needs.
Without discipline, you get untested refactors, hidden business logic loss (a branch that looks unused may handle a rare exception), and audit gaps where no one can show what changed, why, and who approved it.
Automating the Testing Groundwork
Unit test generation is a promising use case for AI agents. Instead of writing every test by hand, engineers can use agents to accelerate tests around existing code. Exadel approached this with Exadel Colleague, a governed, asynchronous SDLC peer that works inside existing Jira and Git workflows.
The goal wasn’t to let AI rewrite a legacy system. It was to cut the manual effort of establishing a testing baseline. A ticket arrives in Jira, testing agents generate TDD and BDD tests before implementation, programming agents implement against them, and a pull request opens in the team’s Git workflow. Engineers review before anything merges.
Nearly 7,000 Tests, One Modernization Challenge
In a real-world modernization-readiness use case, Exadel Colleague generated:
- ~7,000 unit tests
- ~90% reduction in eligible testing effort
- 3,200+ human-equivalent hours saved
In a separate financial services use case, Colleague also created 100+ human-equivalent hours in a two-week sprint by validating repeatable maintenance work against a cloned repository and historical backlog.
This doesn’t mean every program becomes 90% faster. Results depend on code quality, existing coverage, platform complexity, ticket clarity, and how much of the work suits agentic execution.
What Do 7,000 Tests Actually Prove?
Volume isn’t quality. Even high coverage can leave gaps: tests that reproduce incorrect existing behavior, missing edge cases, assertions that verify nothing meaningful, and untested interactions between components.
Tests generated from legacy code describe what the system does, not necessarily what it should do. A baseline that captures a bug will faithfully protect that bug.
Difference Testing: Making Legacy Behavior the Spec
In many legacy systems, the real specification is observed behavior. Difference testing uses that fact. It compares the legacy system with the modernized one before deployment, and the question is simple:
When the same input passes through both systems, does the modernized output match the legacy output where it should?
In capital markets, that could mean proving pricing, P&L, risk, or reporting behavior stays consistent after migration. In energy trading, it could mean new commodity classes and risk calculations not disrupting established behavior. In treasury, it could mean confirmation and settlement workflows staying stable through an upgrade. Difference testing gives teams a safety harness. It doesn’t replace human judgment, but it gives reviewers stronger evidence to judge with.
Why Testing Needs Discipline
Edsger Dijkstra famously warned that testing can reveal the presence of bugs but never prove their absence. In modernization, that warning is a design principle, not a reason to skip tests. No suite proves a system correct, so tests must sit inside a process built to reduce uncertainty before anything reaches production:
- Test-first: define the behavior to preserve before implementation begins
- Compare: run difference tests wherever legacy outputs can be checked
- Review: keep every pull request human-reviewed and traceable to a ticket
AI Generates. Engineers Decide What to Trust.
AI-assisted testing doesn’t eliminate engineering judgment. It moves it to where it matters. Reviewing generated tests, engineers ask:
- Would this test fail if the logic broke?
- Does the assertion verify an outcome, or just execute code?
- Are rare edge cases and failure paths covered?
- Do we trust the expected behavior it encodes?
Engineers stay accountable for architecture, security, review, and production ownership. AI handles the repeatable work, so people can spend less time on boilerplate and more on real engineering problems.
Knowing What to Hand to an Agent
AI agents aren’t the right answer for every task. Good candidates are clearly described, testable, bounded, and repetitive, with acceptance criteria and domain experts who can validate the result. Work that stays human-led includes major architecture decisions, unclear business rules, ambiguous regulatory interpretation, and high-risk production changes.
That’s why a scoping phase matters: identify which modules suit AI-assisted modernization, where test coverage is missing, which tickets are too vague, and what outcomes you’ll measure. Modernization programs fail when ambiguity is pushed downstream. Governed AI works best when ambiguity is surfaced early.
Built for Regulated Environments
In such environments, modernization has to satisfy governance as well as engineering. Every AI-assisted change should trace back to a ticket, tests, a reviewer, and an approval, so auditability is designed in from the start rather than bolted on later. Deployment flexibility matters too. The approach we take with the Exadel Colleague platform is designed for private cloud, on-premises, BYOK, and model-agnostic setups, giving organizations control over where code and data live. And because it works inside existing Jira and Git workflows, modernization can run alongside business-as-usual delivery without pausing the business.
