Stop Using Cursor Just for Business Code—Let AI First Fill Your Spring Boot Tests

Why AI Coding Should Start with Tests

Developers are busy debating AI coding tools such as Codex, Claude Code, and Cursor, wondering whether AI can truly boost productivity. The author observes that most people immediately ask Cursor to write Controllers, Services, DTOs, or full CRUD modules, which is useful but misses the real pain point in mature Spring Boot projects.

In practice, the difficulty lies in confidently modifying, deploying, or refactoring code—especially in order, payment, inventory, permission, MQ, Redis, and transaction scenarios—because there is no test safety net. Without tests, developers cannot be sure that changes won’t break existing functionality.

Therefore, the author proposes a new usage pattern: don’t let Cursor write business code first; let it help you add tests to your Spring Boot project. This approach may be less flashy than “AI writes a whole module,” but it is steadier and offers long‑term value.

Cursor’s Advantage for Test Generation

Cursor runs inside the IDE, so it can see the current class, method implementation, and existing test style. It excels at small‑scope, context‑aware tasks, making test generation a perfect fit. For example, consider the method:

public RefundResult applyRefund(RefundRequest request)

This method involves order lookup, status checks, amount validation, duplicate submission detection, record creation, MQ sending, and status updates. Asking Cursor to rewrite it is risky because it may misinterpret business semantics. Instead, asking Cursor to generate tests for this method is safer: it reads the logic, identifies scenarios, and produces test code.

Concrete Refund Scenario

The article lists typical edge cases for a refund service—order not found, invalid status, amount exceeding limit, duplicate requests, MQ failure, etc.—and shows a prompt that asks Cursor to list these test scenarios without generating code first. This step verifies that Cursor understands the business before producing tests.

Common Pitfalls in Prompting AI for Tests

Vague prompts like “add tests for this class” lead to low‑value output: only a single success case, over‑mocked dependencies, or trivial assertions such as assertNotNull(result). The author suggests a detailed prompt specifying the testing framework (JUnit 5 + Mockito), reference test style, required scenarios, and explicit assertion expectations.

High Coverage vs. High Value

Teams often see test coverage rise after AI‑generated tests, yet the tests may not verify core business logic. Effective tests should assert outcomes such as successful refund result, correct amount, status transition, record creation, MQ sending, and proper handling of failure cases.

Tests Reveal Design Issues

When Cursor struggles to generate tests, it may suggest that a method has too many responsibilities, indicating a design problem. The AI can flag methods that are hard to test, prompting developers to refactor for better testability.

Avoid Letting AI Modify Production Code to Pass Tests

If generated tests fail, developers should first analyze the failure. Failures fall into three categories: incorrect test, bug in production code, or unclear business rule. Only after confirming a genuine bug should production code be changed, and even then only with human approval.

Recommended Cursor Test‑Generation Workflow

Select a service with clear boundaries (avoid the most complex callbacks).

Ask Cursor to read the code and list test scenarios without generating code.

Manually review and refine the scenario list.

Instruct Cursor to generate tests based on the approved list, explicitly forbidding changes to production code.

Run the tests; if they fail, analyze the cause before fixing anything.

Review test quality—effective assertions, reasonable mocking, coverage of edge cases, clear naming.

Merge approved tests; if they expose production bugs, create separate tickets to fix them.

This slower, more disciplined process yields a reliable test safety net, enabling confident refactoring, feature addition, and deployment.

Why Experienced Developers Benefit Most

Seasoned developers understand that tests are the foundation for long‑term project evolution. Without a solid test suite, AI‑generated code can introduce hidden risks. By first using AI to bolster the test suite, teams can later let AI assist with bug fixes, refactoring, or new features with far lower danger.

In summary, the author urges developers to stop using Cursor solely for business code and instead let it first supplement tests in Spring Boot projects, especially legacy codebases lacking test coverage.