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Spring Boot TDD ワークフロー

JUnit 5、Mockito、MockMvc、Testcontainers、JaCoCo を使用した Spring Boot 向けテスト駆動開発。機能追加、バグ修正、リファクタリングの際に使用します。

Spring Boot TDD ワークフロー とは?

このスキルは、Spring Boot アプリケーションにおけるテスト駆動開発 (TDD) のベストプラクティスを提供します。JUnit 5 と Mockito を使った単体テスト、MockMvc による Web レイヤーテスト、`@SpringBootTest` を用いた統合テスト、`@DataJpaTest` による永続化テスト、そして Testcontainers を使用した本番環境に近いインテグレーションテストまでをカバーします。また、JaCoCo を用いたコードカバレッジ計測と 80% 以上のカバレッジ目標を設定し、AssertJ や jsonPath を使ったアサーション、テストデータビルダーパターンも推奨します。このスキルは、機能追加、バグ修正、リファクタリングの際に、信頼性が高くメンテナンスしやすいコードを書くための一貫したワークフローを開発者に提供します。

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

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ドキュメント

Spring Boot TDD ワークフロー は何をしますか?

Spring Boot サービス向けの TDD ガイダンス(単体テスト + 統合テストで 80% 以上のカバレッジを目標)。

使用すべきタイミング

  • 新機能やエンドポイントの追加
  • バグ修正やリファクタリング
  • データアクセスロジックやセキュリティルールの追加

ワークフロー

  1. テストを先に書く(失敗することを確認)
  2. テストを通すための最小限のコードを実装
  3. テストがグリーンの状態でリファクタリング
  4. カバレッジを強化する(JaCoCo)

単体テスト(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());
  }
}

パターン:

  • Arrange-Act-Assert
  • 部分的なモックは避け、明示的なスタブを優先
  • バリエーションには @ParameterizedTest を使用

Web レイヤーテスト(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());
  }
}

統合テスト(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());
  }
}

永続化テスト(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

  • Postgres/Redis には再利用可能なコンテナを使用し、本番環境を模倣
  • @DynamicPropertySource を介して JDBC URL を Spring コンテキストに注入

カバレッジ(JaCoCo)

Maven スニペット:

<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>

アサーション

  • 可読性のために AssertJ(assertThat)を推奨
  • JSON レスポンスには jsonPath を使用
  • 例外に対しては assertThatThrownBy(...) を使用

テストデータビルダー

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 コマンド

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

注意: テストは高速で、独立しており、決定論的に保ってください。実装の詳細ではなく、振る舞いをテストしてください。

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.

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