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springboot-verification

Verifikationsschleife für Spring Boot Projekte: Build, statische Analyse, Tests mit Coverage, Sicherheitsscans und Diff-Review vor Release oder PR.

Was ist springboot-verification?

Diese Fähigkeit definiert eine mehrstufige Verifikations-Pipeline für Spring Boot Projekte. Sie wird vor Pull-Requests, nach größeren Änderungen und vor Deployments ausgeführt. Die Pipeline umfasst einen parallelen Build, statische Code-Analyse mit SpotBugs, PMD und Checkstyle, Testausführung mit Coverage-Messung (JaCoCo, Ziel: 80%+), Sicherheitsscans (CVE-Prüfung, Secrets-Erkennung) sowie einen Diff-Review. Die Fähigkeit liefert einen standardisierten Bericht mit dem Status aller Phasen. Sie unterstützt sowohl Maven- als auch Gradle-Builds und enthält konkrete Implementierungsbeispiele für Unit-Tests (Mockito), Integrationstests (Testcontainers) und API-Tests (MockMvc). Der Fokus liegt auf schnellem Feedback und strengen Qualitäts-Gates für zuverlässige Releases.

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

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Dokumentation

Spring Boot Verifikationsschleife

Vor PRs, nach größeren Änderungen und vor dem Deployment ausführen.

Wann zu aktivieren

  • Vor dem Öffnen eines Pull-Requests für einen Spring Boot Service
  • Nach größeren Refactorings oder Abhängigkeits-Upgrades
  • Pre-Deployment Verifikation für Staging oder Produktion
  • Ausführen der vollständigen Build → Lint → Test → Security Scan Pipeline
  • Validierung der Testabdeckung gegen Schwellenwerte

Phase 1: Build

mvn -T 4 clean verify -DskipTests
# oder
./gradlew clean assemble -x test

Falls der Build fehlschlägt, anhalten und beheben.

Phase 2: Statische Analyse

Maven (gängige Plugins):

mvn -T 4 spotbugs:check pmd:check checkstyle:check

Gradle (falls konfiguriert):

./gradlew checkstyleMain pmdMain spotbugsMain

Phase 3: Tests + Coverage

mvn -T 4 test
mvn jacoco:report   # 80%+ Abdeckung prüfen
# oder
./gradlew test jacocoTestReport

Bericht:

  • Gesamttests, bestanden/fehlgeschlagen
  • Abdeckung % (Zeilen/Zweige)

Unit-Tests

Service-Logik isoliert mit gemockten Abhängigkeiten testen:

@ExtendWith(MockitoExtension.class)
class UserServiceTest {

  @Mock private UserRepository userRepository;
  @InjectMocks private UserService userService;

  @Test
  void createUser_validInput_returnsUser() {
    var dto = new CreateUserDto("Alice", "[email protected]");
    var expected = new User(1L, "Alice", "[email protected]");
    when(userRepository.save(any(User.class))).thenReturn(expected);

    var result = userService.create(dto);

    assertThat(result.name()).isEqualTo("Alice");
    verify(userRepository).save(any(User.class));
  }

  @Test
  void createUser_duplicateEmail_throwsException() {
    var dto = new CreateUserDto("Alice", "[email protected]");
    when(userRepository.existsByEmail(dto.email())).thenReturn(true);

    assertThatThrownBy(() -> userService.create(dto))
        .isInstanceOf(DuplicateEmailException.class);
  }
}

Integrationstests mit Testcontainers

Gegen eine echte Datenbank statt H2 testen:

@SpringBootTest
@Testcontainers
class UserRepositoryIntegrationTest {

  @Container
  static PostgreSQLContainer<?> postgres = new PostgreSQLContainer<>("postgres:16-alpine")
      .withDatabaseName("testdb");

  @DynamicPropertySource
  static void configureProperties(DynamicPropertyRegistry registry) {
    registry.add("spring.datasource.url", postgres::getJdbcUrl);
    registry.add("spring.datasource.username", postgres::getUsername);
    registry.add("spring.datasource.password", postgres::getPassword);
  }

  @Autowired private UserRepository userRepository;

  @Test
  void findByEmail_existingUser_returnsUser() {
    userRepository.save(new User("Alice", "[email protected]"));

    var found = userRepository.findByEmail("[email protected]");

    assertThat(found).isPresent();
    assertThat(found.get().getName()).isEqualTo("Alice");
  }
}

API-Tests mit MockMvc

Controller-Ebene mit vollständigem Spring-Kontext testen:

@WebMvcTest(UserController.class)
class UserControllerTest {

  @Autowired private MockMvc mockMvc;
  @MockBean private UserService userService;

  @Test
  void createUser_validInput_returns201() throws Exception {
    var user = new UserDto(1L, "Alice", "[email protected]");
    when(userService.create(any())).thenReturn(user);

    mockMvc.perform(post("/api/users")
            .contentType(MediaType.APPLICATION_JSON)
            .content("""
                {"name": "Alice", "email": "[email protected]"}
                "))
        .andExpect(status().isCreated())
        .andExpect(jsonPath("$.name").value("Alice"));
  }

  @Test
  void createUser_invalidEmail_returns400() throws Exception {
    mockMvc.perform(post("/api/users")
            .contentType(MediaType.APPLICATION_JSON)
            .content("""
                {"name": "Alice", "email": "not-an-email"}
                "))
        .andExpect(status().isBadRequest());
  }
}

Phase 4: Sicherheitsscan

# Abhängigkeits-CVEs
mvn org.owasp:dependency-check-maven:check
# oder
./gradlew dependencyCheckAnalyze

# Secrets im Quellcode
grep -rn "password\s*=\s*\"" src/ --include="*.java" --include="*.yml" --include="*.properties"
grep -rn "sk-\|api_key\|secret" src/ --include="*.java" --include="*.yml"

# Secrets (Git-Historie)
git secrets --scan  # falls konfiguriert

Häufige Sicherheitsbefunde

# Prüfen auf System.out.println (Logger verwenden)
grep -rn "System\.out\.print" src/main/ --include="*.java"

# Prüfen auf rohe Exception-Nachrichten in Antworten
grep -rn "e\.getMessage()" src/main/ --include="*.java"

# Prüfen auf Wildcard-CORS
grep -rn "allowedOrigins.*\*" src/main/ --include="*.java"

Phase 5: Lint/Format (optionales Gate)

mvn spotless:apply   # falls Spotless-Plugin verwendet wird
./gradlew spotlessApply

Phase 6: Diff-Review

git diff --stat
git diff

Checkliste:

  • Keine Debug-Logs hinterlassen (System.out, log.debug ohne Guards)
  • Aussagekräftige Fehler und HTTP-Statuscodes
  • Transaktionen und Validierungen vorhanden, wo nötig
  • Konfigurationsänderungen dokumentiert

Ausgabevorlage

VERIFIKATIONSBERICHT
====================
Build:     [PASS/FAIL]
Statisch:  [PASS/FAIL] (spotbugs/pmd/checkstyle)
Tests:     [PASS/FAIL] (X/Y bestanden, Z% Abdeckung)
Sicherheit:[PASS/FAIL] (CVE-Funde: N)
Diff:      [X Dateien geändert]

Insgesamt: [BEREIT / NICHT BEREIT]

Zu behebende Probleme:
1. ...
2. ...

Kontinuierlicher Modus

  • Phasen bei signifikanten Änderungen oder alle 30–60 Minuten in langen Sitzungen erneut ausführen
  • Kurze Schleife beibehalten: mvn -T 4 test + spotbugs für schnelles Feedback

Denken Sie daran: Schnelles Feedback ist besser als späte Überraschungen. Halten Sie das Gate streng – behandeln Sie Warnungen in Produktionssystemen als Fehler.

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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