Suites that break on every UI change
A renamed button or moved field fails dozens of tests that were testing nothing wrong.
[ Why AI driven automation testing matters ]
Keeping tests working, not writing them, is what slows automation down
AI driven automation testing applies machine learning and language models to the slow, repetitive parts of test automation: drafting tests, fixing locators after UI changes, comparing screens, choosing which tests to run and separating real failures from noise.
Used carelessly, AI produces tests nobody understands and passes nobody trusts. We use it where it saves real time, and keep engineers in charge of what is tested, what is merged and what blocks a release.
Looking at AI across your whole QA process? See Testing with AI →
The problems it solves
Coverage that lags new features
Features ship faster than anyone can write and review tests for them.
Full regression on every change
Pipelines run everything, every time, because nobody can say which tests a change affects.
Hours lost to failure noise
Engineers read logs from flaky and environmental failures before they find the one real bug.
[ How it helps your product ]
What AI driven automation changes in your product
Five outcomes engineering leads ask us about, and where AI genuinely moves each one.
Speed
Drafted tests and suggested repairs shorten the time from story to running test, and change-based selection keeps pipeline runs short.
Coverage
Gap analysis and generated data reach journeys, rules and edge cases the suite was missing.
Release quality
Visual AI catches layout and styling regressions that pass every functional check.
Cost
Less engineer time spent repairing tests and triaging noise, and more spent on new coverage.
Regression stability
Self-healing and flaky-test triage keep the suite green for the right reasons, so a red build means a real problem.
| Conventional automation | AI driven automation | |
|---|---|---|
| Writing new tests | Every step coded by hand | AI drafts from stories and flows; an engineer edits and approves |
| After a UI change | Broken locators fixed one at a time | Healed at runtime or fixes suggested, reviewed before merge |
| Which tests run | The full suite, or a hand-picked subset | Tests picked from what changed, full run on a schedule |
| Screen checks | Pixel diffs full of noise, or none | Visual AI flags changes a person would notice |
| Red builds | Someone reads every log | Failures grouped, with likely causes |
| Who decides what ships | Engineers | Still engineers |
Honest note: AI speeds up the work around tests; it doesn’t replace judgement. Generated tests are drafts, healed locators are suggestions, and nothing becomes a release gate without a person approving it.
[ AI automation capabilities we deliver ]
Fourteen ways AI improves your test automation
Most teams start with self-healing and test drafting on their existing suite, then add visual AI, smarter selection and triage as trust in the output grows.
AI-assisted test creation
Stories, flows, specsTests drafted from user stories, recorded flows and specs, then reviewed, edited and committed by an engineer in your own framework.
What we cover
- Drafts from stories and acceptance criteria in Jira
- Recorded flows turned into maintainable tests
- Engineer review before anything is merged
When it pays offWhen new features ship faster than your team can write tests for them.
Self-healing locators
Healenium, Testim, mablWhen a button moves or an ID changes, the test finds the element by its other attributes and logs the fix for review, instead of failing the build.
What we cover
- Alternative locators tried at runtime
- Every heal logged with before and after
- Permanent fix committed after review
When it pays offWhen small UI changes break dozens of tests every sprint.
Visual AI comparison
ApplitoolsKey screens compared build to build by visual AI that ignores rendering noise and flags the changes a person would notice.
What we cover
- Baselines per browser, device and viewport
- Fewer false alarms than pixel-by-pixel diffs
- Changes approved or rejected in one dashboard
When it pays offWhen layout and styling bugs pass every functional check.
Change-based test selection
Run what mattersTests chosen and ordered from what changed in the code and where failures have happened before, with the full suite still run on a schedule.
What we cover
- Regression impact analysis from changed files and run history
- Priority from defect likelihood and failure history
- Full regression nightly or before release
When it pays offWhen the full suite is too slow to run on every merge.
Flaky-test detection & triage
From run historyTests that pass and fail without any code change are found from run history, quarantined visibly and fixed at the cause.
What we cover
- Flakiness scored from run history
- Quarantine without hiding failures
- Root cause and fix logged
When it pays offWhen the team re-runs red builds until they turn green.
Failure analysis & root-cause suggestions
Less log readingFailures grouped by likely cause, with logs, screenshots and recent changes summarised, so engineers start from a hypothesis rather than a wall of logs.
What we cover
- Similar failures grouped together
- Likely cause: product bug, test issue or environment
- Summary attached to the defect report
When it pays offWhen triaging a red build takes longer than fixing the bug.
Test data generation
Synthetic and variedRealistic, varied test data generated for forms, APIs and edge cases, so real customer data stays out of your tests.
What we cover
- Boundary values and equivalence classes
- Locale, format and volume variations
- Synthetic data instead of copies of production
When it pays offWhen test data is the bottleneck, or a privacy risk.
Coverage gap analysis
What nothing checksStories, user flows and existing tests compared to show which journeys, rules and edge cases nothing checks yet.
What we cover
- Traceability from requirements to tests
- Untested paths ranked by risk
- Coverage dashboard and a backlog of gaps
When it pays offWhen nobody can say with confidence what the suite actually covers.
AI-assisted API test generation
Postman & REST AssuredAPI tests drafted from your specs and collections: happy paths, negative cases and auth checks, reviewed before they join the suite.
What we cover
- Positive, negative and auth cases drafted
- Schema and status checks
- Engineer review before merge
AI-assisted regression for web
Selenium, Playwright, Cypress, WebdriverIOAI added to your existing web regression: faster drafting, self-healing, visual checks and change-based runs, without rewriting the suite.
What we cover
- Works on your current framework
- Self-healing and visual checks added in layers
- Change-based runs in CI
When it pays offWhen a large web suite costs more to maintain than it saves.Web automation testing →
The same AI layers on Android and iOS suites, where changing locators and device variety make maintenance hardest.
What we cover
- Self-healing for mobile locators
- Visual checks across device sizes
- Failure grouping per device and OS
Natural-language & low-code authoring
Testim, mabl, Testsigma, KatalonTests written in plain language or low-code editors, so manual testers and product owners can add coverage while engineers keep the structure clean.
What we cover
- Plain-language or low-code steps
- Shared components reviewed by engineers
- Run in your CI alongside coded tests
When it pays offWhen the people who know the product best can’t write code.
Suite optimisation & deduplication
Leaner suitesDuplicate, overlapping and low-value tests found and merged or retired, so the suite gets faster without losing coverage.
What we cover
- Duplicate and overlapping tests flagged
- Low-value tests retired with your approval
- Run time and coverage compared before and after
When it pays offWhen the suite has grown for years and nobody prunes it.
AI-generated test documentation
Plans, reports, release notesTest plans drafted from sprint scope, run reports with written summaries, test cases documented from recorded sessions, and release notes with pass and fail results, all kept under version control.
What we cover
- Test plans drafted from sprint scope
- Execution reports with AI-written summaries
- Release notes with pass/fail results, version-controlled
When it pays offWhen documentation lags behind every release, or audits depend on it.Audit & documentation →
[ AI across testing types ]
Where else AI strengthens your testing
The same AI layers carry into the other kinds of testing your product needs. Each links to the service that covers it in full.
Functional testing
Test cases generated from how the application behaves, including edge cases manual passes often miss.
More on functional testing →Regression testing
Tests selected and prioritised by what each change affects and where failures have clustered before.
See change-based selection →Performance testing
Load patterns modelled on real-world traffic and bottlenecks flagged early, with JMeter, k6, Gatling, Locust or BlazeMeter.
More on performance testing →Security testing
Scans for OWASP Top 10 issues, misconfigurations and weak authentication, prioritised by risk, with tools such as OWASP ZAP, Burp Suite, Snyk, Tenable and Veracode.
More on security testing →API testing
Request and response checks, contract compliance and early warning of schema drift for REST, GraphQL and microservices.
More on API testing →Mobile testing
Scenarios adapted across real devices and OS versions, with UI inconsistencies flagged automatically.
More on mobile testing →UI/UX testing
Computer vision compares screens across builds to catch layout shifts, misaligned elements and design drift.
More on UI/UX testing →Accessibility testing
Automated WCAG 2.1 and 2.2 checks for alt text, contrast, keyboard navigation, ARIA labels and screen-reader issues, with suggested fixes.
More on accessibility testing →Automated checks catch common issues early. Security and accessibility still need expert manual testing, which our dedicated teams provide.
[ Guardrails ]
How we keep AI under control
Four commitments that apply to every engagement, whatever tools we use.
Engineer review on every change
Every AI-generated test, locator fix and suggestion is reviewed by an engineer before it’s merged.
Your code and data stay yours
No client code or data is sent to public AI tools without your written approval.
Your models, if you prefer
We can work with AI models you approve, including self-hosted ones.
People own the release gates
AI never decides pass or fail on its own. Release gates are set and approved by people.
[ How you benefit from working with us ]
What you get from QAonTOP’s AI driven automation service
Beyond the tools: how we work with your team, and what you keep when the engagement ends.
Platforms or open source, your call
We work with AI platforms such as Testim, mabl and Testsigma, and with AI layers on open-source frameworks, so we recommend what fits your stack, budget and data rules.
AI on top of what you have
We add AI to your current Selenium, Playwright, Cypress, Appium or API suite rather than asking you to start again.
Code you own
Tests and AI configuration live in your repository, with documentation. No proprietary layer you can’t leave.
A clear audit trail
Every heal, generated test and suggested fix is logged with who reviewed it, so you always know why the suite looks the way it does.
People who know the product
Our manual testers and automation engineers review AI output against how your product should really behave.
Flexible ways to engage
A fixed-scope pilot, managed testing, a dedicated team or an engineer in your sprints. See all six.
[ Technology & tool stack ]
Our AI driven automation technology stack
We pick each layer with your team, based on your current framework, your pipeline and your rules on AI and data. On the right is an example layout.
AI test platformsQagentify · Testim · mabl · Functionize · Postqode · Stably · Testsigma · Katalon (AI features)
Visual AIApplitools
Self-healingHealenium, plus built-in healing in Testim and mabl
AI assistants & agentsGitHub Copilot · ChatGPT / OpenAI · Claude · Playwright MCP / AI agents
AI/ML frameworksTensorFlow · PyTorch · Scikit-learn · Hugging Face Transformers · LangChain
FrameworksSelenium · Playwright · Cypress · Appium · WebdriverIO · Robot Framework (RPA)
APIPostman · REST Assured
CI/CDJenkins · GitHub Actions · GitLab CI · Azure DevOps · CircleCI · Docker · ArgoCD
ExecutionBrowserStack · Sauce Labs · LambdaTest
PerformanceJMeter · k6 · Gatling · Locust · BlazeMeter
SecurityOWASP ZAP · Burp Suite · Snyk · Tenable · Veracode
Reporting & trackingAllure · Extent Reports · Jira · TestRail
Sample project layoutTypeScript · Playwright · AI layer
tests/ e2e/ checkout.spec.ts signup.spec.ts drafts/ (AI drafts, reviewed before merge) visual/ home.visual.spec.ts pages/ CheckoutPage.ts LoginPage.ts ai/ prompts/ story-to-test.md heal-log/ heals.json selection/ changed-files.ts data/ generated-users.json playwright.config.ts .github/workflows/ai-tests.yml
Example only. Your layout follows your framework, tools and team.
AI tools, and where each fits best
Qagentify
Drafting tests and suggesting fixes for broken locators, with an engineer reviewing each change.
Testim
AI-stabilised web tests with smart locators and low-code authoring.
mabl
Low-code web and API tests with auto-healing, run in the cloud.
Functionize
AI test automation with machine-learning-based healing for large web suites.
Postqode
AI agents that help create, run and maintain tests.
Stably
AI-assisted creation and upkeep of end-to-end web tests.
Applitools
Visual AI comparison across browsers and devices.
Healenium
Open-source self-healing for Selenium-based suites.
Testsigma
Natural-language test authoring across web, mobile and API.
Katalon (AI features)
AI-assisted authoring and healing inside a low-code tool.
GitHub Copilot
Helping engineers write and refactor test code faster.
ChatGPT / OpenAI
Drafting test ideas, cases and data, with approval before any client material is used.
Claude
Drafting and reviewing tests and summarising failures, under the same approval rule.
Playwright MCP & AI agents
AI agents that drive a real browser through Playwright to explore flows and draft tests.
Playwright testing services →Comparing frameworks in depth? See our tool-agnostic testing services →
[ How we work ]
Six steps from readiness review to AI your team can run
You see AI working on one suite first, and you approve each stage before we roll it out further.
-
Step 1 of 6
Readiness review
We look at your suite, pipeline, failure history and AI and data rules, and pick where AI will save the most time.
You getAI automation readiness report -
Step 2 of 6
Pilot on one suite
One or two AI layers, such as self-healing or test drafting, added to a single suite so you can judge the results.
You getAI-assisted tests in your repoVisual baseline set -
Step 3 of 6
Guardrails and integration
AI tools configured and tuned on your application using approved data, with review steps, models and quality gates wired into your CI.
You getCI pipeline integration -
Step 4 of 6
Scale
More AI layers and more suites, in the order that pays back fastest.
You getAI-assisted suiteCoverage map -
Step 5 of 6
Run, heal and triage
Suites run on agreed triggers. Heals, flaky tests and failures are reviewed and logged.
You getExecution reportsLocator-fix logFlaky-test triage reportDefect reports -
Step 6 of 6
Hand over
How each AI layer works and how to review its output, documented and walked through with your team, then ongoing tuning and expert support if you want it.
You getDocs and handover
[ Deliverables ]
What you receive
Everything below is yours to keep, whether we stay on to run the suite or hand it over to your team.
AI automation readiness & feasibility report
Where AI will save time in your suite, which tools fit, and what to leave as it is.
Framework source code in your repository
Committed to your repo, under your access controls, with AI layers clearly separated.
AI-assisted test suite with coverage map
Which journeys are covered, which tests were AI-drafted, and what comes next.
Self-healing & locator-fix log
Every heal and suggested fix, what changed, and whether it was accepted.
Flaky-test triage report
Unstable tests, their likely causes and the fixes made.
Visual baseline set
Approved baselines for key screens, per browser and device.
CI pipeline integration
AI layers, change-based runs and quality gates in your CI.
Execution reports per run
Allure or Extent reports, with grouped failures and likely causes.
Defect reports in your tracker
Steps, evidence and the failure summary, logged in Jira.
Documentation & handover session
How each AI layer works, how to review its output, and a live walkthrough.
[ Ways to work ]
Six ways to work with our AI automation engineers
A new suite, a rescue of an existing one, ongoing runs or an engineer in your team.
01Fixed-price projectChoose it for a new framework or a migration with a clear scope.02Managed testingChoose it when you want the suite built, run and maintained for you.03Dedicated QA teamChoose it when automation grows every sprint.04Staff augmentationChoose it when your team needs an AI automation specialist for a while.05QA consultingChoose it for a review of your current suite and a plan to improve it.06On-demand testingChoose it for a one-off push, such as stabilising a flaky suite.
Hire our QAHire an automation engineerAn automation engineer who joins your sprints, works in your repository and grows coverage with your team.See the role →
Start with no obligationFree AI automation consultationTalk to an automation engineer about your current suite, your tools and your rules on AI and data. We’ll suggest where automation pays off first.Book the consultation →
Not sure which fits? Find your model in four questions, compare all six or estimate your cost.
[ FAQ ]
AI driven automation testing questions, answered
{{ f.a }}
[ Contact us ]
Talk to an AI automation engineer
✓ Free one-week POC✓ NDA before access✓ A QA lead replies the same day
[ Keep exploring ]