STABLE RUN

Your existing suites just run through Stable Run. It heals broken locators live during every run, shows which tests a change affects, lets the local AI debug and fix code failures — and writes new tests from requirements or recorded walkthroughs. All on one Mac mini.

Runs entirely inside your intranet. The local AI never sends anything out — and can be extended to a cloud provider only if you choose.

During release cycles, front-end changes rename or move elements and suites go red. Stable Run recovers them live from each element's fingerprint — automatically, on every run including unattended Bamboo runs. The run stays green and script-maintenance effort drops.

Across the 100 most common drift types, 98 healed to the correct element on the local model — web & mobile. Every heal is logged with its confidence.

Run every test case once as a green (passed) run. That baseline teaches the AI each element's real fingerprint — so when the app changes later, it re-points the element on its own. More clean runs, sharper healing.

Target.the("pay button")
  .locatedBy(
    "/html/body/div[2]/form/div[3]/button[1]"
  );

Target.the("pay button")
  .locatedBy(
    "[data-testid='bill-pay-submit']"
  );

Self-healing repairs a break after it happens. Optimize finds the fragile locators while they still work — position-based paths, deep chains — and recommends the sturdier address the element already carries. Reviewed in the console, never applied silently.

locatedBy("[data-testid='transfer-amt']")

locatedBy("[data-testid='transfer-amount']")

Automated runs self-heal on their own. When a fix needs code, a person clicks — the AI analyses, fixes, tests and commits it. Nothing lands unless it passes.

A QA lead or business tester launches any scenario from the console — no code, no command line. If it fails on a code issue, they hand it to the AI: it analyses the failure, proposes a fix, re-tests it green, then documents and commits — with a person in the loop to approve.

Pick a change — uncommitted edits, a commit, or a branch/PR from a list — and Stable Run traces it to the exact scenarios that use it. Run just those, not the whole suite.

Shared steps count too: change the sign-in screen and every scenario that logs in through it is selected — each with the reason shown. Load the impacted subset straight into execution and run it — one click.

Stable Run maps each change to the exact scenarios that use it, so a targeted run replaces a full one — shorter feedback, sharper regression. A build or shared-fixture change still triggers the full suite.

As a customer I want to send money to a
saved payee so that I can pay my bills
without re-entering their details.

Feature: Transfer to a saved payee
  Scenario: Send money to a saved payee
    Given James is on the transfers page
    When James transfers "50.00" to "Acme Utilities"
    Then James should see the transfer confirmation

Tidy Given/When/Then is used as-is; free prose is structured by the local AI. Generated steps reuse your existing step library wherever possible, so the test binds and runs on day one — and it's tagged for a person to review before it joins the suite.

A tester records a walkthrough — in the browser, or on a real Android/iOS device via the Mobile Inspector. Every click, keystroke and check is captured, and the AI turns it into a readable scenario plus the runnable code behind it: step definitions, page objects, dependable locators. Review it, save it — it's part of the suite.

A QA lead or business tester can run a suite, watch it, and let the AI fix a failure — without touching a terminal.

Steps 1–3 bring your ready suites online; step 4 folds in the rest.