Communitygithub.com

Spring Boot TDD 工作流程

針對 Spring Boot 服務的測試驅動開發,使用 JUnit 5、Mockito、MockMvc、Testcontainers 與 JaCoCo。適用於新增功能、修復錯誤或重構時使用。

Spring Boot TDD 工作流程 是什麼?

Spring Boot TDD 技術技能為開發者提供了一套結構化的測試驅動開發工作流程,適用於 Spring Boot 服務,目標是達到 80% 以上的程式碼覆蓋率(包含單元測試與整合測試)。此技能支援多種測試層級:以 JUnit 5 與 Mockito 撰寫的單元測試、使用 MockMvc 的 Web 層測試、透過 @SpringBootTest 進行的整合測試,以及採用 @DataJpaTest 與 Testcontainers 的持久層測試。技能強調「先寫測試,讓測試失敗」,然後實現最小程式碼讓測試通過,最後在測試保持通過的情況下重構。此外,還整合了 JaCoCo 來強制覆蓋率標準,並推薦 AssertJ 的 assertThat 與 jsonPath 進行可讀性高的斷言。整體設計目的是保持測試快速、隔離且可預測,著重於測試行為而非實作細節。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/springboot-tdd

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Spring Boot TDD 工作流程 是做什麼的?

針對 Spring Boot 服務的 TDD 指引,目標達到 80% 以上覆蓋率(單元 + 整合)。

使用時機

  • 新增功能或端點
  • 修復錯誤或重構
  • 新增資料存取邏輯或安全規則

工作流程

  1. 先撰寫測試(測試應該失敗)
  2. 實作最小程式碼讓測試通過
  3. 在測試通過的情況下重構
  4. 強制覆蓋率標準(JaCoCo)

單元測試(JUnit 5 + Mockito)

@ExtendWith(MockitoExtension.class)
class MarketServiceTest {
  @Mock MarketRepository repo;
  @InjectMocks MarketService service;

  @Test
  void createsMarket() {
    CreateMarketRequest req = new CreateMarketRequest("name", "desc", Instant.now(), List.of("cat"));
    when(repo.save(any())).thenAnswer(inv -> inv.getArgument(0));

    Market result = service.create(req);

    assertThat(result.name()).isEqualTo("name");
    verify(repo).save(any());
  }
}

模式:

  • Arrange-Act-Assert
  • 避免部分模擬;偏好明確的 stub
  • 使用 @ParameterizedTest 處理變體

網頁層測試(MockMvc)

@WebMvcTest(MarketController.class)
class MarketControllerTest {
  @Autowired MockMvc mockMvc;
  @MockBean MarketService marketService;

  @Test
  void returnsMarkets() throws Exception {
    when(marketService.list(any())).thenReturn(Page.empty());

    mockMvc.perform(get("/api/markets"))
        .andExpect(status().isOk())
        .andExpect(jsonPath("$.content").isArray());
  }
}

整合測試(SpringBootTest)

@SpringBootTest
@AutoConfigureMockMvc
@ActiveProfiles("test")
class MarketIntegrationTest {
  @Autowired MockMvc mockMvc;

  @Test
  void createsMarket() throws Exception {
    mockMvc.perform(post("/api/markets")
        .contentType(MediaType.APPLICATION_JSON)
        .content("""
          {"name":"Test","description":"Desc","endDate":"2030-01-01T00:00:00Z","categories":["general"]}
        """))
      .andExpect(status().isCreated());
  }
}

持久層測試(DataJpaTest)

@DataJpaTest
@AutoConfigureTestDatabase(replace = AutoConfigureTestDatabase.Replace.NONE)
@Import(TestContainersConfig.class)
class MarketRepositoryTest {
  @Autowired MarketRepository repo;

  @Test
  void savesAndFinds() {
    MarketEntity entity = new MarketEntity();
    entity.setName("Test");
    repo.save(entity);

    Optional<MarketEntity> found = repo.findByName("Test");
    assertThat(found).isPresent();
  }
}

Testcontainers

  • 使用可重複使用的容器來模擬 Postgres/Redis 以貼近生產環境
  • 透過 @DynamicPropertySource 將 JDBC URL 注入 Spring 上下文

覆蓋率(JaCoCo)

Maven 設定片段:

<plugin>
  <groupId>org.jacoco</groupId>
  <artifactId>jacoco-maven-plugin</artifactId>
  <version>0.8.14</version>
  <executions>
    <execution>
      <goals><goal>prepare-agent</goal></goals>
    </execution>
    <execution>
      <id>report</id>
      <phase>verify</phase>
      <goals><goal>report</goal></goals>
    </execution>
  </executions>
</plugin>

斷言

  • 偏好使用 AssertJ 的 assertThat 以提高可讀性
  • 對於 JSON 回應,使用 jsonPath
  • 對於例外處理:assertThatThrownBy(...)

測試資料建構器

class MarketBuilder {
  private String name = "Test";
  MarketBuilder withName(String name) { this.name = name; return this; }
  Market build() { return new Market(null, name, MarketStatus.ACTIVE); }
}

CI 指令

  • Maven:mvn -T 4 test 或 mvn verify
  • Gradle:./gradlew test jacocoTestReport

請記住:保持測試快速、隔離且可預測。測試行為,而非實作細節。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

相關技能