Every IBM i leader knows the tension: your applications are stable, proven, and packed with decades of refined logic, but the world around them is changing fast. Retiring RPG/COBOL talent, rising integration demands, and pressure to innovate mean futurization isn’t optional anymore.
What is optional is how risky you let transformation become.
Because transforming mission-critical IBM i applications isn’t just a conversion problem. It’s a trust problem. The business will only move forward if IT can prove, before anything touches production, that transformed applications behave the same way legacy systems do today.
That confidence doesn’t come from spot checks or optimism.
It comes from automated testing built specifically for transformation.
Why Transformation Testing Is Different From “Normal” Testing
Most automated testing tools were designed for new development. They excel at validating what a new application should do.
Transformation introduces a different requirement: you have to validate what a new application does compared to what the old one already does. That means proving functional equivalence — fidelity testing — across real workflows, real data, and real execution paths.
In IBM i environments, that’s uniquely challenging. Legacy ecosystems are often large, deeply interdependent, and rich with logic that isn’t fully documented because it lives in the code itself. Standard tools assume expected outcomes are clearly defined in advance, tests can be planned cleanly from requirements, and failure patterns will resemble new-build defects.
Those assumptions rarely hold in transformation. Trying to force traditional automation into a fidelity role becomes too time-intensive, too expensive, and still incomplete. And in transformation, incomplete testing isn’t just an inefficiency — it’s risk.
Transformation testing has to do more than confirm the application works. It has to prove it works the same way as the legacy system — because your business logic isn’t speculative. It’s already running the company.
That’s why automated fidelity testing isn’t a nice extra in futurization. It’s one of the guardrails that makes safe, incremental change possible, especially when legacy and futurized components are running side-by-side through coexistence.
The Real Standard Futurization Testing Must Meet
When you futurize an IBM i application, you’re not trying to invent something new. You’re preserving what’s proven while unlocking what’s next — which means the bar for “done” is higher than in normal development. The business already trusts these workflows because they’ve been running reliably for years. Futurization can only move forward if that trust transfers cleanly to the futurized version.
That’s why transformation testing isn’t about checking whether the new app works in isolation. It’s about proving, with evidence, that the futurized application behaves the same way as the legacy one across real-world usage — including the edge cases users don’t document but depend on every day
Transformation testing has to answer questions like:
- Does every workflow behave the same way?
- Do calculations match line-for-line on the same paths?
- Do database updates occur identically, in the same sequence?
- Are screens and data presented consistently across each step?
- Does performance stay within acceptable bounds?
It isn’t enough for the futurized system to “work.” It has to work the same, with evidence to prove it.
Our Approach: Automated Testing Built for IBM i Futurization
This is the gap our automated transformation testing methodology was designed to close. Instead of relying on manually written test scripts or incomplete requirements, our automated testing starts with what your application actually does today.
It records real workflows on the original application — whether they run through a 5250/Genie interface, Rich Display Files, or batch processes — and automatically captures the UI state at each step along with the related database changes. That recording becomes your baseline, created without writing test code.
Once the application is futurized, our automated testing replays those same workflows against the reimagined version and compares behavior side-by-side. Any divergence in screens, data, execution flow, or performance is flagged immediately — and drills down to show where and why the difference occurred.
This is why our automated testing is fundamentally different from “standard automation.” It’s designed to prove equivalence first, not just functionality.
What Automated Testing Checks (& Why That Matters)
Where most tools stop at pass/fail, our automated testing has to go deeper because fidelity requires it. It compares screens, navigation flow, database changes, execution paths, code coverage, calculations, and performance to confirm functional equivalence across the lifecycle.
A key part of that proof is screen-flow recording and replay for both 5250 and Rich Display Files. Because user experience is part of functional equivalence, we compare the exact UI payloads and navigation steps from legacy to futurized versions — not just backend outcomes. If the futurized application renders screens differently, populates fields inconsistently, or changes flow timing, we catch it.
The result is simple: you don’t just know something failed — you know what changed and why.
The Real Breakthrough: Automated Diagnosis & AI-Assisted Fixing
One of the hidden costs in transformation testing isn’t finding discrepancies. It’s diagnosing root cause fast enough to maintain momentum.
Our automated testing reduces that drag.
When it detects divergence, it highlights the exact data difference and the specific line of code responsible, with side-by-side views of old and new logic. It also shows where execution paths diverged so your team can see the full context of the mismatch.
From there, we can optionally engage agentic AI to propose a fix, re-run the equivalence tests, and iterate until the discrepancy is fully resolved — or escalated to a human developer when needed.
Testing doesn’t become a gate that slows futurization. It becomes the engine that keeps it safe and moving forward.
Why This Matters for Incremental Futurization
Here’s the bigger point: our fully AI-enabled environment and fidelity automation is what makes coexistence-driven futurization viable at scale.
When you futurize in slices — a program, a table, a workflow at a time — you need to validate each slice quickly and confidently. Without transformation-specific automation, slices naturally grow into chunks, risk concentrates, and value gets delayed. The project drifts toward big-bang dynamics.
With automated testing, each slice can be proven, deployed, and measured safely. That’s how organizations see progress early, reduce risk continuously, and keep futurization aligned to business priorities.
A Living Safety Net for DevOps & Agentic Delivery
Because tests are recorded once and replayed continuously, they become a living safety net, not a one-time validation moment. They can run headless in batches, in parallel, and inside CI/CD pipelines, so equivalence testing stays enforced as the futurized system evolves.
That matters even more post-transformation, when autonomous agents begin executing complete development tasks. Agents don’t just write code in this model, they compile, test, validate, and present evidence. Automated testing is what makes that safe, repeatable, and scalable over time.
Your Next Step: Futurize Confidently, Not Cautiously
IBM i futurization is about amplifying what’s proven, not replacing it blindly. But amplification only works when every step is validated with confidence.
That’s what automated transformation testing delivers.
If you want to see automated testing in action, check out the video below or reach out to Futurization@ProfoundLogic.com. We’ll show how automated equivalence validation fits alongside coexistence and hybrid refactoring to make incremental futurization safe, fast, and measurable.