From End-to-End to Scalable: AN Engineering Technical Test Maturity Model for the Real World

I’ve lost count of how many times I’ve sat in meetings and heard something like:

“We need more test coverage. Let’s add another 500 end-to-end tests.”

It sounds logical, right? More tests = more coverage = more confidence. But anyone who’s worked in software engineering long enough knows the reality: every new end-to-end test is a bit like buying another luxury car for a daily commute. It looks impressive in the parking lot, but the costs pile up, maintenance becomes a nightmare, and suddenly you’re stuck in traffic anyway.

The truth is, not all tests are equal. And if we want to scale — really scale — we need to get smarter, not just bigger.

The Model: Engineering Technical Test Maturity vs. Scalability

Oh no, not another Engineering or QA model, but wait here me out it might be the model that helps put some of the others in context. For background this also ties in to the Test Pyramid Revisited I wrote back in 2018.

Here’s the mental model I use with teams: picture a two-axis graph.

• X-axis: Engineering Technical Test Maturity → How well the engineering team designs and distributes tests across levels of the system (unit, service/component, integration, end-to-end)
• Y-axis: Scalability → How easily your test suite can grow without spiraling into cost and chaos.

Where you sit on this graph says everything about how sustainable your testing approach is.

The Four Quadrants

1. Bottom-Left (Low Maturity, Low Scalability):
   Everything is tested through the UI. Tests are slow, flaky, and expensive. The team spends more time fixing broken test scripts than actually improving quality. Releases drag.
2. Bottom-Right (Higher Maturity, Still Limited Scalability):
   The team has invested in some unit and integration tests, but the balance is still skewed. Scalability is limited because there’s still too much weight on bulky end-to-end flows.
3. Top-Left (Low Maturity, Attempted Scaling):
   These are the danger zones. Teams try to “scale” by multiplying end-to-end tests — buying more cloud platform licenses, running suites in parallel — but it’s duct tape. The costs grow linearly, and every release cycle feels heavier. Is it sustainable though?
4. Top-Right (High Maturity, High Scalability):
   The sweet spot. Tests are distributed smartly across the pyramid. Most are small, fast, and cheap; a handful of end-to-end flows validate the user journey. Costs stay stable even as coverage grows. Releases are fast, feedback loops are tight.

So where do you fit in this model? Challenge yourselves and realistically try to plot your company against this model in terms of Maturity vs Scalability.

Why End-to-End Feels So Safe (But Isn’t)

End-to-end tests are seductive. They simulate the user journey, they’re easy to explain to stakeholders, and they give the illusion of “covering everything.” But here’s what happens when you lean too heavily on them:

• Every test is potentially slow. One flow = minutes. Multiply by hundreds = hours.
• Infrastructure explodes. More VMs, more licenses, more environments = $$$.
• Flakiness rises. The more complex the flow, the more likely it fails for reasons unrelated to defects.
• Maintenance burns out teams. A single UI tweak or API response change can break dozens of tests.

It’s coverage on paper, not in practice.

The 80/20 Rule: Why Balance Wins

The pragmatic target is 80/20:

• 80% → Unit, service, component, and integration tests.
• 20% → End-to-end tests validating full workflows.

This ratio balances confidence with scalability. You still see the full user journey, but you’re not drowning in it.

Real-World Case Study

Let’s compare two hypothetical teams:

Team A (E2E Heavy):

• 500 end-to-end tests.
• Average runtime: 2 minutes/test → 16 hours total.
• Infrastructure cost: $5,000/month.
• Regression cycle: 2–3 days.
• Developer morale: low (lots of flaky failures).

Team B (Balanced):

• 400 unit/integration tests (<5s each).
• 80 component tests (30s each).
• 20 end-to-end tests (2m each).
• Total runtime: <45 minutes.
• Infrastructure cost: $1,200/month.
• Regression cycle: <1 hour.
• Developer morale: high (fast feedback, less noise).

Both claim “coverage,” but only one is scalable.

The Hidden Economics of Testing

This is where leaders often get surprised. Optimizing your test mix doesn’t just improve quality and scalability — it saves real money.

• Scenario: A team reduces their end-to-end suite from 70% of coverage down to 20%.
• Result: Infra spend drops 50–70%. Test runtime shrinks from hours to under an hour.
• Knock-on Effect: Faster builds → faster releases → faster time-to-market.

When you frame it this way, engineering test maturity isn’t just a technical decision — it’s a business strategy.

Steps to Move Up the Curve

Think of this as your roadmap out of E2E quicksand:

1. Audit your suite. How many tests are end-to-end vs. unit/integration? Be brutally honest.
2. Break down big flows. Start with one journey (e.g., “user login + transfer”) and split it into smaller pieces.
3. Upskill your team. If your testers are mostly non-technical, invest in training. Pair them with devs, run coding workshops, start small with service tests.
4. Invest in tooling. Use frameworks and technologies that make lower-level automation fast and maintainable.
5. Track ROI. Show leadership the time and cost saved after shifting coverage. It’s your best argument for continued investment. Make sure to snapshot what your testing and cost looks like today and how that gradually shifts into green as you advance in this maturity
6. Introduce AI & Predictive Testing. Modern tools can help select the right subset of tests to run, cutting even further into cycle time while maintaining confidence. It also helps to understand your code repos, understanding technical architecture, helps in writing more lower level tests fast (or helps with quicker adoption at this level)

The Future of Scalability: AI and Predictive Testing

We’re entering a new era but instead of moving head strong blindly, take a balanced approach. Instead of brute-forcing more tests, practically maybe you could approach it as follows:

• AI-based test generation. Tools that create unit and service tests automatically from code changes.
• Predictive test selection. Running only the tests most likely to fail, based on history.
• Intelligent monitoring. Linking production telemetry back to test prioritization.

The engineering technical test maturity vs scalability model still applies, but now the journey to the top-right corner is getting accelerators, we are more equipped now to get there faster than we ever were in the past

Wrapping It Up

Scaling testing isn’t about adding more tests. It’s about adding smarter tests.

• If you’re stuck in the bottom-left, ask yourself: what’s one flow we can break down today?
• If you’re duct-taping your way in the top-left, ask: how long until this collapses under cost and flakiness?
• If you’re climbing toward the top-right — congrats. You’re not just improving your engineering test maturity, you’re future-proofing delivery.

The next time someone says “we need more coverage, why did our testing not find that production issue”, pull up this model and ask: “Do you want more tests, or do you want more scalability?”

See also : (https://toyerm.wordpress.com/2018/10/16/lower-level-automation-and-testing-be-more-precise-the-automation-triangle-revisited-again/)