Standard Checks: The Missing Base Layer of Software Quality
Software has become the backbone of modern life, yet it still lacks the kind of baseline safety and quality standards that other engineering fields take for granted. Even the best testing teams often miss common issues because they focus on complex business logic while overlooking basic, repeatable checks. Standard Checks close this gap by providing an AI-driven, automated layer of static and dynamic tests that catch serious escaped bugs, ensure broad coverage across accessibility, privacy, security, and usability, and free human testers to focus their creativity where it matters most: business-specific risks and unique product logic.
It’s crazy how ad hoc software testing still is today. Every team has a different mix of testers with different experiences, tools, pressures, and maturity levels. The very idea of “quality” in software is inconsistently defined and inconsistently measured.
Think about it: in most engineering disciplines, there are clear baseline standards. Electronics get tested by UL Labs for overheating, interference, or safety. Devices must pass FCC tests before hitting the market. These industries have accepted that there must be a minimum, standardized layer of checks.
Software — arguably the fabric of modern society — has no such baseline. Instead, we hand the responsibility to whatever testers happen to be on the team and hope they “do a good job.”
Why Standard Checks Haven’t Existed — Until Now
Historically, software testing has been too expensive, time-consuming, and labor-intensive to scale. That’s why only the biggest, wealthiest companies could afford automation and broad coverage. For everyone else, testing stayed narrow, specialized, and inconsistent.
AI changes that equation. For the first time, software testing gains reuse:
Reuse enables scale
Scale enables standardization
Standardization enables comparison of quality across products and teams
With AI, the impossible suddenly becomes possible.
The Myth of “Every App Is Unique”
Most engineers believe their software is a one-of-a-kind unicorn. The reality? Most software looks very similar.
Every app has:
Sign-up and login screens
Product listings
Credit card entry
Error messages
APIs and database schemas
The components repeat, over and over, just in different flavors. If users can install the Starbucks app and reorder coffee within minutes, it’s only because apps follow familiar, reusable design patterns.
That convergence of design and features means standard checks are not only possible, they’re necessary.
Precedents Already Exist
We already have WCAG for accessibility, OWASP for security, GDPR for privacy. These are frameworks of essential checks, accepted globally. What’s been missing is a unified, automated layer of standard checks across all the common functionality of modern software.
Static vs. Dynamic Checks
Standard Checks fall into two broad categories: static and dynamic.
Static checks can be performed by analyzing screenshots, logs, and other artifacts without requiring live interaction. They are more like asking, “Look at this — see any bugs?” Examples include catching broken layouts in a screenshot, spotting JavaScript errors in a log, or reviewing whether error messages are clear.
Dynamic checks, on the other hand, involve interactive input/output flows. They are closer to Selenium scripts, exploratory testing, or user flows. Dynamic checks simulate a user or script by actually clicking, typing, and moving through a flow, verifying whether everything behaves as expected end to end.
Both types are essential: static checks provide fast, broad coverage with minimal overhead, while dynamic checks validate the deeper functionality of critical user flows.
Static and General Checks
Networking behavior and traffic
JavaScript behavior and errors
Generative AI features and flows
User interface quality and issues
Security vulnerabilities
Privacy compliance
Accessibility standards compliance
Mobile usability and responsiveness
Site content quality and clarity
Generative AI outputs
Error message clarity and quality
Chatbot user experience and behavior
GDPR compliance
OWASP security guidelines
WCAG accessibility standards
Console log output and errors
Element-level accessibility
Dynamic and Feature-Specific Checks
Search box behavior and validation
Search results quality
Product detail pages
Product catalog pages
News and article content
Shopping cart functionality
Signup and registration workflows
Social profile pages
Checkout and payment flows
Social feed content and flow
Landing and marketing pages
Homepage content and layout
Contact and support pages
Pricing page clarity and usability
About/team/company pages
System error content
Video and media-rich content
Legal and policy content
Careers and job listings
Forms and validation
Booking and reservation workflows
Cookie consent flows
Address and shipping flows
See details for each set of Standard Checks: https://testers.ai/standard_checks.html
How the AI Runs Standard Checks
The AI executes Standard Checks in a structured sequence:
- Artifact Collection and Static Checks
Collect all available artifacts — screenshot, underlying code, network traffic, and console logs — and run a full suite of static, general checks across this matrix. - Page Understanding and Feature Identification
Classify the page content and detect its key features (such as a search box, sign-in dialog, or checkout flow), then run feature-specific static checks relevant to those elements. - Persona Generation and Qualitative Feedback
Generate likely user personas and simulate their qualitative feedback on the page experience. Complement this with targeted interactive test generation. - Dynamic Test Execution
Produce and execute dynamic tests — covering happy paths, edge cases, invalid inputs, negative flows, and scenarios statistically likely to expose bugs — similar to exploratory testing or a Selenium regression suite. - Issue Triage and Validation
Use a dedicated evaluation agent to deduplicate findings, validate correctness, assign priority, and filter for relevance. - Optional Human Review
Present the refined issue list for expert review, where human testers can thumbs-up, thumbs-down, or star findings to confirm or highlight them. - Quality Report and Developer Integration
Generate a polished quality report summarizing AI-found and human-reviewed results. Integrate directly with bug tracking systems and, when running inside developer IDEs like Cursor, Windsurf, or VS Code, attach a “Copilot fix prompt” to each issue — a ready-to-use snippet developers can paste into their coding agent to accelerate fixes.
Enter “Standard Checks” from Testers.AI
That’s what we at Testers.AI are building. We call them Standard Checks:
A base layer of automated, reusable checks for the functionality nearly every piece of software shares
AI-driven, so they can be applied at scale
Standardized, so quality assessments can finally be compared across products, companies, and industries
We’re democratizing the kind of automated quality validation that was once reserved for the richest companies — like when our team built systems to load every XBOX app at Microsoft or thousands of Android apps at Google.
Now, thanks to modern AI, this isn’t just for the elite. It’s for everyone.
Why It Matters
Software is now infrastructure. It runs businesses, powers economies, and touches every part of our daily lives. It’s time to treat software quality as an engineering discipline — with a shared, repeatable baseline of checks.
How to Run Standard Checks
Run the Standard Checks yourself, manually or via automation scripts. Or:
Standard Checks are the future of software quality.
If you’d like to get Standard Checks running in your IDE: https://cotestpilot.ai
If you’d like to get Standard Checks running with just a button click, or in your CI/CD system, checkout https://testers.ai
If you would like someone to simply run the standard checks for you: https://icebergqa.com
— Jason Arbon, CEO @testers.ai, and Principal @ IcebergQA