Scenario Discovery
AI maps stories, logs, and production usage into executable coverage.
AspectIQ turns product context into autonomous testing decisions, self-healing execution, release risk signals, and fast QA guidance your team can actually trust.
AI maps stories, logs, and production usage into executable coverage.
Broken locators and unstable selectors are recovered automatically.
Every run gets a confidence signal with reasons teams can review.
Release confidence after AI validation
Designed to feel like the homepage platform story, but focused on the concrete AI jobs that save QA teams time every day.
Reads stories, analytics, and defect history to suggest what should be tested first.
Generates realistic paths, negative cases, and data combinations with product context attached.
Repairs unstable selectors and adapts when UI changes threaten automation reliability.
Groups failures into explainable patterns so teams triage once instead of repeatedly.
Combines flake history, dependency health, and test results into a readable ship signal.
Answers natural-language questions and turns prompts into runnable testing actions.
Instead of acting like a black box, AspectIQ exposes the planning, execution, and reasoning behind each AI recommendation.
Requirements, analytics, incidents, and production behavior shape what AI tests first.
Critical flows are expanded into positive, negative, and edge-case scenarios automatically.
Healing decisions, flaky dependencies, and release blockers are surfaced in one view.
AI starts from requirements, analytics, usage patterns, and known failure areas.
It expands scenarios around risky journeys instead of generating shallow test noise.
Runs across web, mobile, and APIs while handling change and instability automatically.
Outputs a confidence signal with evidence that engineering and QA can review together.
Move faster with autonomous planning, self-healing execution, and release intelligence that stays visible to your team.