Built fast with AI. Checked properly before launch.
AI coding tools can produce a working app in days. Working is not the same as safe, complete or ready for real users. We test the flows, access rules, inputs and configuration of AI-generated apps and report what to fix before launch.
Overview
What it is. AI-generated app testing is a pre-launch quality review for software built largely with AI coding assistants and app builders, such as Cursor, Claude, Lovable or Bolt. We test whether the app does what it should, whether users can reach only what they should, and whether the configuration is ready for the real world.
Who it is for. Founders and product teams who built a prototype or product with AI tools, and engineering teams whose code increasingly comes from AI assistants and who want an independent check before release.
What you get. A prioritised list of defects and risks with the steps to reproduce each, a plain statement of what was and was not covered, and regression checks that protect the fixes.
Why it matters
The problem it addresses. Generated code looks finished long before it is. Common gaps are broken edge cases, missing or weak access control, unvalidated input, exposed keys and settings, and behaviour that nobody on the team has read closely.
When it matters most. Before the first real users, before the app handles payments or personal data, and when a prototype is about to become a product.
What happens without it. Users find the broken flows, one account can reach another’s data, and a configuration mistake is discovered after it has been exploited.
What we test
Functional correctness
end-to-end flows, forms, validation and error handling checked against what the app is meant to do
Authentication and access control
sign-in, sessions, roles and whether one user can reach another user’s data
Input handling
validation, injection-style input and file uploads, at QA level
Secrets and configuration
exposed keys, debug settings, open endpoints and environment differences
Data rules and storage
database access rules, data validation and what is stored where
Dependencies
outdated, unnecessary or unfamiliar packages that the generated code pulled in
Edge cases and failure paths
empty states, slow networks, repeated actions and what happens when something fails
Browsers, devices and accessibility
behaviour across the browsers and devices real users have, with basic accessibility checks
How UpDoer approaches AI-Generated App Testing
Understand
Requirements, tickets, code diffs and product context.
We learn what the app is for, how it was built, which tools and services it uses and what data it will hold, and agree what must work before launch.
Risk Mapping
Where a failure would cost the most.
Flows and data are ranked by what a failure would cost, with sign-in, payments, personal data and anything that changes records first.
Test Strategy
Coverage chosen, test cases drafted.
We decide which flows get scripted checks, which get exploratory sessions, and what QA-level security probing is in scope, so effort goes where risk is highest.
Execute
Manual and automated testing runs.
Scripted and exploratory testing runs against a staging copy where possible, including access-control probing from the point of view of a second user.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps explore generated flows, draft edge-case inputs, scan configuration and dependency lists, and group similar defects so review starts from patterns.
Human Verification
A QA engineer checks every flagged result.
A QA engineer reproduces each issue, separates real defects from noise, and rates severity by what it means for your users and data.
Report & Improve
The verified quality signal, then better coverage.
Findings are reported by severity with steps to reproduce and the coverage limits stated. Fixed issues become regression checks you can keep running.
AI-Generated App Testing: from launch risk to release signal
01
Where it breaks
Broken edge casesOpen accessExposed keys02
Test workflow
FlowsAccessInputConfig03
AI assists
Explore generated flowsScan config and packagesGroup similar defects04
Humans decide
Try it as a second userDecide launch blockers05
Release signal
Coverage by riskOpen defectsGo / no-go by a person
Illustrative workflow. Not a client result or a measured outcome.
AI-generated app coverage matrix
| Quality area | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Functionality | Core flows, forms, validation, error handling | Explores flows and drafts edge-case inputs | Judges correctness for the product |
| Access control | Sign-in, sessions, roles, cross-account access | Suggests access scenarios to try | Tries them as a second user and rates impact |
| Input handling | Validation, hostile input, uploads | Generates unusual and malformed inputs | Confirms whether an issue is exploitable in context |
| Configuration | Keys, debug settings, open endpoints | Scans configuration for exposure | Verifies and prioritises each finding |
| Dependencies | Outdated or unneeded packages | Lists packages that merit review | Decides which matter for this app |
| Compatibility | Browsers, devices, basic accessibility | Drafts a matrix from the target audience | Reviews rendering and usability findings |
Quality area: Functionality
- WHAT WE VALIDATE
- Core flows, forms, validation, error handling
- AI ASSISTANCE
- Explores flows and drafts edge-case inputs
- HUMAN VALIDATION
- Judges correctness for the product
Quality area: Access control
- WHAT WE VALIDATE
- Sign-in, sessions, roles, cross-account access
- AI ASSISTANCE
- Suggests access scenarios to try
- HUMAN VALIDATION
- Tries them as a second user and rates impact
Quality area: Input handling
- WHAT WE VALIDATE
- Validation, hostile input, uploads
- AI ASSISTANCE
- Generates unusual and malformed inputs
- HUMAN VALIDATION
- Confirms whether an issue is exploitable in context
Quality area: Configuration
- WHAT WE VALIDATE
- Keys, debug settings, open endpoints
- AI ASSISTANCE
- Scans configuration for exposure
- HUMAN VALIDATION
- Verifies and prioritises each finding
Quality area: Dependencies
- WHAT WE VALIDATE
- Outdated or unneeded packages
- AI ASSISTANCE
- Lists packages that merit review
- HUMAN VALIDATION
- Decides which matter for this app
Quality area: Compatibility
- WHAT WE VALIDATE
- Browsers, devices, basic accessibility
- AI ASSISTANCE
- Drafts a matrix from the target audience
- HUMAN VALIDATION
- Reviews rendering and usability findings
Where AI helps
- Exploring generated flows and drafting edge-case inputs that a prototype’s own tests are unlikely to include.
- Scanning configuration and dependency lists for settings and packages that merit a human look.
- Grouping similar defects so one underlying cause is not reported many times.
- Summarising results into a readable launch-readiness note that a QA engineer confirms.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Judging whether a flow is really correct for the product, not just whether it runs.
- Trying access the way a second user or a curious visitor would, which scripts rarely do.
- Deciding which findings are launch blockers and which can follow.
- Stating the limits clearly: this is QA-level checking, not a penetration test, certification or security audit.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
Is this a penetration test?
No. It is QA-level testing that includes checks for common access-control, input and configuration problems. It is not a penetration test, a code audit or a security certification, and we state its limits in every report.
Do you review the generated code?
Our focus is how the app behaves. If you give us the code or repository we can use it to find causes and to check dependencies and configuration, and we would agree that scope with you first.
Can you test an app that is still a prototype?
Yes, and it is often the cheapest time to find problems. We agree what must be solid before launch and test against that, not against an imagined finished product.
Does it matter which AI tool built the app?
Not for the testing approach. Different tools tend to leave different kinds of gaps, so knowing what was used helps us look in the right places.