Most AI migration tools treat existing unit tests as a prerequisite. If you don't have them, the migration can't be validated. We generate comprehensive test suites from scratch — by understanding what your application actually does — and optionally capture real production workflows to achieve coverage that static analysis alone never could.
Tools like GitHub Copilot take a Strangler Fig approach — migrating the application file by file, leaning on whatever unit tests already exist to validate each piece. That works for greenfield rewrites of small modules. It breaks down on real enterprise applications.
One file migrated at a time. Each migrated file validated against existing unit tests — if they exist.
Tests generated from what the application does — not what tests someone wrote years ago. Validated end-to-end against an isolated replica.
The pipeline runs sequentially. Each stage builds on the last. Two stages are always applied — they are the core of what makes our testing work. One stage is optional but high-impact, and one is a targeted boost when conditions allow.
Deterministic tools + LLMs build a complete picture of application behavior before any migration begins.
Our deterministic analysis tools parse the entire application — every page, every code-behind file, every shared library — building a structural graph of the solution. An LLM then analyzes the output of those tools to extract intent: the business rules, user flows, data interactions, and implicit state machines that the code encodes but never explicitly documents.
The result is a specification of what the application does, expressed independently of how it does it. This specification is the source from which all test cases are generated.
The key insight: You don't need existing test cases to generate test cases. You need to understand what the application does — and that is exactly what this step produces. Pre-existing tests, if you have them, are incorporated as an additional signal — not a prerequisite.
Embed lightweight instrumentation in the live application to capture real user workflows — with full privacy protection.
Static analysis tells you what the application can do. Runtime capture tells you what it actually does in production. When access to the running original application is available, we embed a lightweight, read-only instrumentation library that observes user interactions over a period of time — capturing the actual sequences, inputs, and state transitions that real users perform every day.
Privacy-first by design — built into the instrumentation layer.
All captured data passes through a privacy filter before it is stored anywhere. Real values are never recorded. The instrumentation captures only the shape of data: a string of 12 characters, a date within a certain range, an integer between two values. Your users' actual data never leaves your environment — not to us, not to any third party.
What it captures
What it never captures
Coverage impact: Applications often have hundreds of code paths that never appear in developer-written test cases because developers don't know those paths exist. Runtime capture surfaces them automatically — dramatically increasing the fidelity and completeness of the generated test suite.
A fully automated replica of the original runtime environment — including a generated test database — for safe, repeatable execution.
Tests can only be meaningful if they run in a controlled environment. We automate the construction of an isolated replica of the original application's runtime — same framework version, same configuration, same external dependencies stubbed to known behavior. Critically, this environment runs both the original application and the migrated application in parallel, enabling direct output comparison at the HTTP, DOM, and data layer.
A realistic test database is generated automatically from the data shapes captured in Stages 1 and 2. It has the right structure, the right value ranges, and the right relational integrity — without containing a single piece of real user data.
The original and migrated applications run simultaneously against the same test database. Every response, every output, every database write is compared — divergences are flagged precisely, not just reported as pass/fail.
The isolated environment is fully reproducible. Tests can be run repeatedly — during active development, after every batch of migrations, and during final acceptance — without touching production data or infrastructure at any point.
Tests generated from everything gathered, then executed end-to-end against the migrated application. Functional equivalence, scored per flow.
Our tooling consumes the full output of the previous stages — the semantic model from Stage 1, enriched with real workflow traces from Stage 2 where available — and generates a complete, executable test suite. These tests do not assert unit-level implementation details. They assert functional equivalence: given the same input, does the migrated application produce the same result as the original?
The goal is 100% functional equivalence. In practice, some residual manual testing will be needed — subjective UI behavior and edge cases that no tool can fully anticipate. But the more of this pipeline that runs, the smaller that residual becomes. And for most applications, what remains is a known, bounded, manageable surface — not an open-ended unknown.
The pipeline compounds. Every stage builds on the last, expanding the coverage of the generated test suite in ways that no single approach can achieve alone.
Functional equivalence coverage estimates based on internal project data. Results vary by application complexity, size, and available runtime access.
Your migration is not held hostage by the quality of your existing test coverage. Whether you have thousands of unit tests or none at all, we build the test foundation from first principles. Pre-existing tests are an accelerant — not a gate.
The test suite runs continuously as migration progresses — not just at a final UAT milestone. Every batch of migrated code is validated immediately. Divergences are caught and addressed in context, not discovered weeks later during system testing.
Manual QA effort is not eliminated — it is focused. The automated pipeline identifies exactly where equivalence gaps remain, so your testers spend time on the cases the tools cannot resolve — not re-validating what is already verified.
The more of this pipeline that runs in your environment, the smaller the manual surface becomes — and the more confident every stakeholder can be at go-live.
Every engagement starts with a free assessment. We will analyze your codebase, identify the test generation approach that applies to your situation, and give you a realistic estimate of the equivalence coverage we can achieve — before you commit to anything.