How We Achieve 80%+ Test Coverage from Day One on Every Project

Why Most Teams Fail at Testing

There is a pattern we see over and over when we audit codebases built by other teams. The application logic is there. The features work (mostly). But the test suite is either nonexistent or woefully incomplete, hovering somewhere between 10% and 30% coverage.

Why? Because testing is almost always the first thing to get cut when deadlines tighten. Teams build the feature, promise themselves they will "add tests later," and then move on to the next sprint. Later never comes. The backlog of untested code grows. Every new release becomes a gamble.

This is not a discipline problem. It is a capacity problem. Writing thorough tests by hand takes significant time. A developer who spends an hour building a feature might need another hour or two to write proper unit tests, integration tests, and edge case coverage for it. When you multiply that across every feature in a sprint, the math simply does not work under tight deadlines.

"We will add tests in the next sprint" is the most expensive sentence in software development. Every week without coverage is a week where bugs silently compound.

Our Approach: Tests Written Alongside Code, Every Time

At RG INSYS, we made a decision early on: tests are not optional, and they are never deferred. Every feature ships with its test suite. Every pull request includes coverage for the code it introduces. This is not aspirational. It is how we actually work, on every project, for every client.

The reason we can deliver on this promise without blowing timelines is simple: we use LLM coding agents to generate tests alongside the application code. Tools like Claude Code, Cursor, and GitHub Copilot do not just write features for us. They write the test suites too.

Here is how the process works in practice:

• Feature implementation: A developer (working with an AI agent) builds the feature or module.
• Immediate test generation: The same agent, or a second pass, generates unit tests, integration tests, and edge case scenarios for the new code.
• Human review: Our engineers review both the application code and the generated tests, verifying correctness, adjusting assertions, and ensuring meaningful coverage.
• CI enforcement: The pull request cannot merge unless coverage thresholds are met. This is enforced automatically in our pipelines.

The result is that test coverage is not something we "achieve" at the end of a project. It is built into the fabric of every commit from the very first day.

The Testing Stack We Use

Different layers of an application need different kinds of testing. We use a combination of tools depending on the project:

Unit and Component Testing: Vitest and Jest

For unit tests, we rely on Vitest (for Vite based projects) and Jest (for broader Node.js ecosystems). These tools let us test individual functions, utilities, API handlers, and React or Vue components in isolation. LLM agents are particularly effective here because unit tests follow predictable patterns: given these inputs, expect these outputs. The agents generate tests for happy paths, null and undefined inputs, boundary values, type mismatches, and error states.

End to End Testing: Playwright

Playwright is our default for end to end (E2E) testing. It allows us to simulate real user interactions across browsers, including navigation, form submissions, authentication flows, and complex multi step workflows. AI agents generate Playwright scripts that cover the critical user journeys, and our QA engineers refine them to handle timing, network conditions, and flaky test mitigation.

API Testing

For backend services and REST or GraphQL APIs, we write automated tests that validate response schemas, status codes, authentication and authorization rules, pagination, and error handling. These run on every push and catch regressions before they reach staging.

How LLM Agents Generate Comprehensive Test Suites

The real force multiplier is not just that AI agents can write tests. It is what kinds of tests they write. A human developer under time pressure will typically write a few happy path tests and call it done. An LLM agent, given the right prompts and context, will generate:

• Happy path tests: The expected behavior when everything goes right.
• Edge cases: Empty strings, zero values, maximum length inputs, special characters, unicode.
• Boundary conditions: Off by one errors, pagination limits, rate limiting thresholds.
• Error handling: Network failures, invalid tokens, malformed payloads, timeout scenarios.
• State transitions: What happens when a user performs actions out of order, or when concurrent requests collide.

This breadth of coverage is what moves a project from 20% to 80%+ in a single sprint. The agents handle the volume, and our engineers handle the judgment calls: deciding which tests are meaningful, which assertions matter, and which scenarios are worth the maintenance cost.

Real Metrics: 80%+ Coverage vs the Industry Average

In the codebases we inherit, test coverage is usually well short of where it should be, and much of what exists is concentrated in the easiest to test utility functions. Critical business logic, API endpoints, and user facing flows are frequently undertested.

On RG INSYS projects, we consistently hit 80% or higher statement coverage within the first two weeks of development. On mature projects that have been running for several months, coverage often climbs above 85%. These are not vanity numbers. They represent real tests that run in CI on every commit and catch real bugs before they reach production.

To be specific about what this looks like:

• A recent SaaS platform we built reached 83% coverage by the end of the first sprint (two weeks).
• A fintech API project achieved 87% coverage within three weeks, with full integration test suites for every endpoint.
• A legacy modernization project (rewriting a PHP monolith in Node.js) hit 81% coverage on the new codebase before the first staging deployment.

The Compounding Benefit of Early Coverage

High test coverage from day one is not just about catching bugs today. It creates a compounding advantage that grows over the life of the project:

Fewer Bugs in Production

This one is obvious, but the scale matters. Projects with 80%+ coverage see dramatically fewer production incidents than projects with sparse testing. Bugs that would have taken hours to debug in production are caught in seconds during CI.

Cheaper Maintenance

When you have a strong test suite, adding features or fixing issues becomes significantly cheaper. Developers can make changes with confidence, knowing that the tests will catch unintended side effects. Without tests, every change is a risk, and teams slow down as the codebase grows.

Confident Refactoring

Refactoring without tests is like performing surgery blindfolded. With comprehensive coverage, our team can restructure code, upgrade dependencies, and optimize performance without fear of breaking existing functionality. This keeps the codebase healthy and prevents the slow accumulation of technical debt that plagues most projects.

Faster Onboarding

Tests serve as living documentation. When a new developer joins the project, the test suite tells them exactly what the code is supposed to do, how edge cases are handled, and what the expected behavior looks like in each scenario. This accelerates onboarding and reduces the time new team members need to become productive.

When 100% Coverage Is Overkill

We aim for 80%+ coverage, not 100%. That distinction is intentional. Chasing 100% coverage leads to diminishing returns and, in some cases, actively harmful testing practices.

Here is what we do not waste time testing:

• Trivial getters and setters that contain no logic.
• Framework boilerplate that is already tested by the framework maintainers.
• Pure configuration files where the "test" would just duplicate the config values.
• Third party library internals that are outside our control.

Instead, we focus our testing energy where it has the highest impact:

• Business logic and domain rules: The code that directly represents your revenue generating processes.
• API endpoints and data transformations: Where incorrect behavior directly affects users.
• Authentication and authorization flows: Where bugs have security implications.
• Complex state management: Where subtle bugs are most likely to hide.

This focused approach means that our 80%+ coverage number represents meaningful, high value tests, not inflated metrics from testing trivial code.

What This Means for Your Project

If you are evaluating software development partners, ask them one simple question: what is your test coverage target, and when do you start testing?

If the answer is "we add tests after the features are complete" or "we aim for coverage in the final sprint," that is a red flag. Testing deferred is testing denied. You will end up with a codebase that works today but becomes increasingly expensive and risky to maintain over time.

At RG INSYS, we deliver 80%+ automated test coverage from day one on every project. It is baked into our process, enabled by AI coding agents, and enforced by our CI pipelines. The result is software that is not just built fast, but built to last.

Frequently asked questions

What is a good test coverage target for a new software project?

RG INSYS targets 80% or higher automated test coverage, not 100%. Chasing 100% leads to diminishing returns and tests of trivial code. The 80%+ target is spent on business logic, API endpoints, authentication and authorization flows, and complex state management, where bugs are most costly and most likely to hide.

How do AI coding agents help increase test coverage?

AI coding agents generate tests alongside application code, covering happy paths, edge cases, boundary conditions, error handling, and state transitions that developers under time pressure often skip. Engineers then review the generated tests, adjust assertions, and decide which scenarios are worth the maintenance cost. This is how a project moves from 20% to 80%+ coverage in a single sprint.

How quickly can a project reach 80% test coverage?

On RG INSYS projects, 80% or higher statement coverage is consistently reached within the first two weeks of development. For example, a recent SaaS platform reached 83% by the end of its first two week sprint, and a fintech API project reached 87% within three weeks. Coverage thresholds are enforced in CI on every pull request.

Which testing tools are used to achieve 80%+ coverage?

RG INSYS uses Vitest for Vite based projects and Jest for broader Node.js ecosystems to unit test functions, API handlers, and React or Vue components. Playwright handles end to end tests across browsers, and automated API tests validate response schemas, status codes, authentication rules, pagination, and error handling on every push.

Sources and further reading

• Code Coverage Best Practices, Google Testing Blog (2020). Google engineers on why there is no single ideal coverage number and why chasing the last few percent has diminishing returns.
• Test Coverage, Martin Fowler (2012). Why coverage works best for finding untested code rather than as a target, and why 100% should raise suspicion.
• Configuring Jest, Jest. The coverageThreshold option, which fails the test run when coverage drops below a set minimum.
• coverage | Config | Vitest, Vitest. Coverage threshold settings for minimum line, function, branch, and statement coverage.
• Installation | Playwright, Playwright. End to end testing for web apps across Chromium, WebKit, and Firefox.

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