What We Learned Generating 10,000 Load Tests Automatically

From One Test to Ten Thousand

When we first built Barcable's automatic test generation, we expected hundreds of runs to validate the idea. Instead, we crossed ten thousand before the quarter ended.

That scale changed everything. We started seeing patterns, optimizations, and opportunities for refinement that only appear when automation meets real-world diversity. What began as an experiment became a data-rich exploration of how far autonomous testing can go.

Why We Ran 10,000 Tests

Barcable automatically generates k6 load tests from repository context — OpenAPI specs, routes, and fixtures — and executes them as managed Cloud Run jobs.

Our goal was to test whether AI-generated load suites could stay realistic and reliable across hundreds of services. To find out, we ran large-scale trials on:

• 400+ Dockerized repositories
• 10,000+ generated test suites
• Millions of requests executed across multiple environments

Can AI-generated tests match human-authored ones?

They can — and in many cases, they do it faster. Automated generation provides consistent, reproducible baselines that human engineers can refine and compare over time.

What Emerged First: The Discovery Phase

Running at this scale revealed not problems, but patterns. Each test reflected how real repos are structured and deployed.

We learned how factors like base URLs, data seeding, and fixture completeness shaped performance outcomes — valuable input that makes each new generation smarter.

For instance, one automatically generated suite surfaced a deployment still pointing to a temporary endpoint. The insight wasn't a "bug" — it was a signal that autonomous tests can act as early observability tools for deployment health.

By run number five thousand, our heuristics were self-adjusting and producing even more production-faithful scenarios.

Patterns We Discovered

When the dataset grew, clear performance laws emerged.

Average p95 latency across all services stabilized near 280 ms, with the widest variance tied to specific high-traffic endpoints.

The Pareto Pattern of Performance

Across most repositories, 20% of endpoints drove 80% of total latency — the Pareto principle, confirmed through autonomous testing at scale.

We also saw:

• Regression detection: ~37% of automated tests caught performance shifts before deployment.
• Throughput variation: closely linked to the quality of seeded data and fixture diversity.

How Automation Evolved Our Workflow

As Barcable matured, the role of testing evolved from manual scripting to continuous insight.

Features like Auto-Run, Toggle Runs, and live p95 metrics made performance validation part of everyday CI/CD.

Instead of waiting for late-stage testing, teams now check load and latency in near real-time — just like checking unit test results.

Lessons for Teams Scaling Autonomous Testing

Here are the best practices we refined from ten thousand automated runs:

• Seed realistic data early — representative fixtures ensure valid load profiles.
• Start compact — run short smoke tests before endurance profiles.
• Use generated suites as baselines — evolve them with each iteration.
• Track metric drift — compare latency and error rate trends between releases.

Case in Point: Continuous Confidence

Teams that integrated Barcable generation into every PR saw measurable impact: up to 40% fewer performance regressions within two release cycles.

The Next 10,000 Tests

Reaching 10,000 runs taught us that automation amplifies human capability. Autonomous doesn't mean hands-off — it means smarter, faster, and measurable.

Our next step: correlating test data with production telemetry to build predictive reliability scores — so teams can anticipate bottlenecks before they appear.