Platform / Test Automation
Your manual test scripts are now your automation.
Write tests in plain English — or bring the manual scripts you already have. AI reads each step, sees the page like a tester, and acts. No automation code. No selectors. Nothing to maintain.
If developers coordinate AI agents instead of hand-coding every behavior, why should testers still hand-code every validation?
- Write automation scripts
- Repair selectors and locators
- Maintain brittle frameworks
- Spend your time fixing tests
- Write tests in plain English
- Let AI execute and recover
- Validate outcomes by vision
- Keep a full decision record
of automation initiatives fail within two years.
of QA time goes to maintaining tests, not writing them.
AI tools pass Monday and fail Tuesday — with no app change.
Sources: industry research, incl. Capers Jones and enterprise QA surveys.
Ten layers of resilience for tests that refuse to break.
Every step runs an escalation chain. When one layer can't act, the next takes over — and the result is verified before anything is marked complete.
AI selector generation
Selectors built at runtime from the live page, not hard-coded.
Semantic validation
Confirms the element matches the step's intent before every click.
Fallback escalation
Up to three AI alternatives, each semantically validated.
Coordinate vision
Sees the element visually when selectors fail entirely.
DOM resolution
Maps the visual hit to the real clickable element.
Nearby semantic search
A keyword-ranked search finds the right target in the region.
Compound step decomposition
Splits multi-action instructions into atomic operations.
Post-action verification
AI vision confirms the expected outcome after each step.
Adaptive overlay handling
Popups and consent banners stop blocking runs.
Ambiguity + rewrite
Flags vague steps and suggests clearer wording.
Above the chain sit two more layers: selector memory makes proven runs deterministic, and self-healing turns failures into one-click fixes.
Four capabilities that work as one.
The advantage isn't any single feature — it's how they reinforce each other on every run.
Selector memory
Proven runs replay deterministically in about two seconds and cut AI cost 60–80%. Every cached click is re-validated against the step's intent before it executes.
Self-healing with integrity
Heals how a step runs, never what it asserts. Fixes are proposed for one-click approval, so a genuine application defect still fails — on record, with the attempt documented.
Automated failure triage
Every failure arrives pre-classified — application defect, test defect, environment, or data — with evidence, so review starts at the diagnosis instead of the logs.
Autonomous Explorer
Point it at a URL with a budget and guardrails; it probes paths nobody scripted and converts each finding into a runnable regression test.
The AI Decision Inspector shows what the AI saw, what it chose, the alternatives it considered, and whether the step came from a fresh decision or a cached recipe — including every healing rewrite, shown side by side with the original. Enterprise AI adoption requires trust, and trust requires transparency.
Nine levels separate a script runner from an intelligent platform.
Most tools stall between levels 1 and 3. These are the capabilities Spec2TestAI brings to every run — which does your current tool deliver today?
Behind-firewall agent
Outbound HTTPS only — no inbound ports, no VPN, no IT tickets.
Cloud parallel fan-out
50 test cases finish in the wall-clock time of one.
Shadow DOM & enterprise UIs
Salesforce, SAP, and Oracle front-ends traversed automatically.
CI/CD pipeline ready
GitHub Actions, Jenkins, and Azure DevOps via dedicated API keys.
Choose your AI provider
OpenAI, Azure OpenAI in your tenant, or AWS Bedrock.
Regression & stability scoring
Every run categorized; flaky cases flagged automatically.
Bring your hardest application.
Break a test on purpose — then watch the platform diagnose it, heal it, and hand you the fix in one click.
Request a demo →