tdd-guide

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TDD Guide - Test Driven Development for Engineering Teams

TDD指南 - 面向工程团队的测试驱动开发

A comprehensive Test Driven Development skill that provides intelligent test generation, coverage analysis, framework integration, and TDD workflow guidance across multiple languages and testing frameworks.

这是一项全面的测试驱动开发技能，可在多语言和多测试框架下提供智能测试生成、覆盖率分析、框架集成及TDD工作流指导。

Capabilities

核心功能

Test Generation

测试用例生成

Generate Test Cases from Requirements: Convert user stories, API specs, and business requirements into executable test cases
Create Test Stubs: Generate test function scaffolding with proper naming, imports, and setup/teardown
Generate Test Fixtures: Create realistic test data, mocks, and fixtures for various scenarios

从需求生成测试用例：将用户故事、API规范和业务需求转换为可执行的测试用例
生成测试桩代码：生成具备规范命名、导入语句及初始化/清理逻辑的测试函数脚手架
生成测试夹具：为各类场景创建真实的测试数据、模拟对象及夹具

TDD Workflow Support

TDD工作流支持

Guide Red-Green-Refactor: Step-by-step guidance through TDD cycles with validation
Suggest Missing Scenarios: Identify untested edge cases, error conditions, and boundary scenarios
Review Test Quality: Analyze test isolation, assertions quality, naming conventions, and maintainability

指导红-绿-重构流程：通过步骤化引导完成TDD周期并进行验证
提示缺失场景：识别未被测试的边缘情况、错误条件及边界场景
评审测试质量：分析测试隔离性、断言质量、命名规范及可维护性

Coverage & Metrics Analysis

覆盖率与指标分析

Calculate Coverage: Parse LCOV, JSON, and XML coverage reports for line/branch/function coverage
Identify Untested Paths: Find code paths, branches, and error handlers without test coverage
Recommend Improvements: Prioritized recommendations (P0/P1/P2) for coverage gaps and test quality

计算覆盖率：解析LCOV、JSON和XML格式的覆盖率报告，获取行/分支/函数覆盖率数据
识别未测试路径：找出无测试覆盖的代码路径、分支及错误处理逻辑
提出改进建议：针对覆盖率缺口和测试质量提供优先级划分（P0/P1/P2）的改进建议

Framework Integration

框架集成

Multi-Framework Support: Jest, Pytest, JUnit, Vitest, Mocha, RSpec adapters
Generate Boilerplate: Create test files with proper imports, describe blocks, and best practices
Configure Test Runners: Set up test configuration, coverage tools, and CI integration

多框架支持：适配Jest、Pytest、JUnit、Vitest、Mocha、RSpec等框架
生成样板代码：创建具备正确导入、描述块及最佳实践的测试文件
配置测试运行器：设置测试配置、覆盖率工具及CI集成

Comprehensive Metrics

全面指标分析

Test Coverage: Line, branch, function coverage with gap analysis
Code Complexity: Cyclomatic complexity, cognitive complexity, testability scoring
Test Quality: Assertions per test, isolation score, naming quality, test smell detection
Test Data: Boundary value analysis, edge case identification, mock data generation
Test Execution: Timing analysis, slow test detection, flakiness detection
Missing Tests: Uncovered edge cases, error handling gaps, missing integration scenarios

测试覆盖率：行、分支、函数覆盖率及缺口分析
代码复杂度：圈复杂度、认知复杂度、可测试性评分
测试质量：单测试断言数、隔离性评分、命名质量、测试坏味检测
测试数据：边界值分析、边缘场景识别、模拟数据生成
测试执行：耗时分析、慢测试检测、不稳定测试检测
缺失测试：未覆盖的边缘场景、错误处理缺口、缺失的集成场景

Input Requirements

输入要求

The skill supports automatic format detection for flexible input:

该技能支持自动格式检测，输入方式灵活：

Source Code

源代码

Languages: TypeScript, JavaScript, Python, Java
Format: Direct file paths or copy-pasted code blocks
Detection: Automatic language/framework detection from syntax and imports

支持语言：TypeScript、JavaScript、Python、Java
格式：直接提供文件路径或粘贴代码块
检测：通过语法和导入语句自动检测语言/框架

Test Artifacts

测试工件

Coverage Reports: LCOV (.lcov), JSON (coverage-final.json), XML (cobertura.xml)
Test Results: JUnit XML, Jest JSON, Pytest JSON, TAP format
Format: File paths or raw coverage data

覆盖率报告：LCOV（.lcov）、JSON（coverage-final.json）、XML（cobertura.xml）
测试结果：JUnit XML、Jest JSON、Pytest JSON、TAP格式
格式：文件路径或原始覆盖率数据

Requirements (Optional)

需求（可选）

User Stories: Text descriptions of functionality
API Specifications: OpenAPI/Swagger, REST endpoints, GraphQL schemas
Business Requirements: Acceptance criteria, business rules

用户故事：功能的文本描述
API规范：OpenAPI/Swagger、REST端点、GraphQL模式
业务需求：验收标准、业务规则

Input Methods

输入方式

Option A: Provide file paths (skill will read files)
Option B: Copy-paste code/data directly
Option C: Mix of both (automatically detected)

选项A：提供文件路径（技能将读取文件）
选项B：直接粘贴代码/数据
选项C：混合使用（自动检测）

Output Formats

输出格式

The skill provides context-aware output optimized for your environment:

该技能提供上下文感知的输出，针对不同环境优化：

Code Files

代码文件

Test Files: Generated tests (Jest/Pytest/JUnit/Vitest) with proper structure
Fixtures: Test data files, mock objects, factory functions
Mocks: Mock implementations, stub functions, test doubles

测试文件：生成符合Jest/Pytest/JUnit/Vitest规范的测试文件
夹具：测试数据文件、模拟对象、工厂函数
模拟实现：Mock实现、桩函数、测试替身

Reports

报告

Markdown: Rich coverage reports, recommendations, quality analysis (Claude Desktop)
JSON: Machine-readable metrics, structured data for CI/CD integration
Terminal-Friendly: Simplified output for Claude Code CLI

Markdown：丰富的覆盖率报告、改进建议、质量分析（适用于Claude桌面端）
JSON：机器可读的指标数据，适用于CI/CD集成
终端友好格式：精简输出，适用于Claude Code CLI

Smart Defaults

智能默认值

Desktop/Apps: Rich markdown with tables, code blocks, visual hierarchy
CLI: Concise, terminal-friendly format with clear sections
CI/CD: JSON output for automated processing

桌面/应用端：带表格、代码块、视觉层级的丰富Markdown格式
CLI：简洁、终端友好的分段格式
CI/CD：JSON格式输出，便于自动化处理

Progressive Disclosure

渐进式披露

Summary First: High-level overview (<200 tokens)
Details on Demand: Full analysis available (500-1000 tokens)
Prioritized: P0 (critical) → P1 (important) → P2 (nice-to-have)

先看摘要：高层级概述（少于200词）
按需查看详情：完整分析内容（500-1000词）
优先级划分：P0（关键）→ P1（重要）→ P2（锦上添花）

How to Use

使用方法

Basic Usage

基础用法

@tdd-guide

I need tests for my authentication module. Here's the code:
[paste code or provide file path]

Generate comprehensive test cases covering happy path, error cases, and edge cases.

@tdd-guide

我需要为我的认证模块生成测试。以下是代码：
[粘贴代码或提供文件路径]

生成覆盖正常流程、错误场景及边缘情况的全面测试用例。

Coverage Analysis

覆盖率分析

@tdd-guide

Analyze test coverage for my TypeScript project. Coverage report: coverage/lcov.info

Identify gaps and provide prioritized recommendations.

@tdd-guide

分析我的TypeScript项目的测试覆盖率。覆盖率报告路径：coverage/lcov.info

识别覆盖率缺口并提供优先级划分的改进建议。

TDD Workflow

TDD工作流

@tdd-guide

Guide me through TDD for implementing a password validation function.

Requirements:
- Min 8 characters
- At least 1 uppercase, 1 lowercase, 1 number, 1 special char
- No common passwords

@tdd-guide

指导我通过TDD流程实现密码验证函数。

需求：
- 至少8个字符
- 包含至少1个大写字母、1个小写字母、1个数字、1个特殊字符
- 不能是常见密码

Multi-Framework Support

多框架支持

@tdd-guide

Convert these Jest tests to Pytest format:
[paste Jest tests]

@tdd-guide

将这些Jest测试转换为Pytest格式：
[粘贴Jest测试代码]

Scripts

核心脚本

Core Modules

核心模块

test_generator.py: Intelligent test case generation from requirements and code
coverage_analyzer.py: Parse and analyze coverage reports (LCOV, JSON, XML)
metrics_calculator.py: Calculate comprehensive test and code quality metrics
framework_adapter.py: Multi-framework adapter (Jest, Pytest, JUnit, Vitest)
tdd_workflow.py: Red-green-refactor workflow guidance and validation
fixture_generator.py: Generate realistic test data and fixtures
format_detector.py: Automatic language and framework detection

test_generator.py：从需求和代码智能生成测试用例
coverage_analyzer.py：解析并分析覆盖率报告（LCOV、JSON、XML）
metrics_calculator.py：计算全面的测试及代码质量指标
framework_adapter.py：多框架适配器（Jest、Pytest、JUnit、Vitest）
tdd_workflow.py：红-绿-重构工作流引导与验证
fixture_generator.py：生成真实的测试数据及夹具
format_detector.py：自动检测语言与框架

Utilities

工具类

complexity_analyzer.py: Cyclomatic and cognitive complexity analysis
test_quality_scorer.py: Test quality scoring (isolation, assertions, naming)
missing_test_detector.py: Identify untested paths and missing scenarios
output_formatter.py: Context-aware output formatting (Desktop vs CLI)

complexity_analyzer.py：圈复杂度与认知复杂度分析
test_quality_scorer.py：测试质量评分（隔离性、断言、命名）
missing_test_detector.py：识别未测试路径与缺失场景
output_formatter.py：上下文感知的输出格式化（桌面端 vs CLI）

Best Practices

最佳实践

Test Generation

测试用例生成

Start with Requirements: Write tests from user stories before seeing implementation
Test Behavior, Not Implementation: Focus on what code does, not how it does it
One Assertion Focus: Each test should verify one specific behavior
Descriptive Names: Test names should read like specifications

从需求出发：在查看实现代码前，先根据用户故事编写测试
测试行为而非实现：关注代码的功能，而非实现方式
单一断言聚焦：每个测试应仅验证一个特定行为
描述性命名：测试名称应像规格说明一样清晰可读

TDD Workflow

TDD工作流

Red: Write failing test first
Green: Write minimal code to make it pass
Refactor: Improve code while keeping tests green
Repeat: Small iterations, frequent commits

红：先编写失败的测试
绿：编写最少的代码使测试通过
重构：在保持测试通过的同时优化代码
重复：小迭代，频繁提交

Coverage Goals

覆盖率目标

Aim for 80%+: Line coverage baseline for most projects
100% Critical Paths: Authentication, payments, data validation must be fully covered
Branch Coverage Matters: Line coverage alone is insufficient
Don't Game Metrics: Focus on meaningful tests, not coverage numbers

目标80%+：大多数项目的行覆盖率基准
关键路径100%覆盖：认证、支付、数据验证等模块必须完全覆盖
分支覆盖率很重要：仅行覆盖率是不够的
不要为指标而指标：关注有意义的测试，而非覆盖率数字

Test Quality

测试质量

Independent Tests: Each test should run in isolation
Fast Execution: Keep unit tests under 100ms each
Deterministic: Tests should always produce same results
Clear Failures: Assertion messages should explain what went wrong

独立测试：每个测试应能独立运行
快速执行：单元测试应控制在100毫秒以内
确定性：测试应始终产生相同的结果
清晰的失败信息：断言消息应明确说明问题所在

Framework Selection

框架选择

Jest: JavaScript/TypeScript projects (React, Node.js)
Pytest: Python projects (Django, Flask, FastAPI)
JUnit: Java projects (Spring, Android)
Vitest: Modern Vite-based projects

Jest：适用于JavaScript/TypeScript项目（React、Node.js）
Pytest：适用于Python项目（Django、Flask、FastAPI）
JUnit：适用于Java项目（Spring、Android）
Vitest：适用于现代Vite生态项目

Multi-Language Support

多语言支持

TypeScript/JavaScript

Frameworks: Jest, Vitest, Mocha, Jasmine
Runners: Node.js, Karma, Playwright
Coverage: Istanbul/nyc, c8

框架：Jest、Vitest、Mocha、Jasmine
运行器：Node.js、Karma、Playwright
覆盖率工具：Istanbul/nyc、c8

Python

Frameworks: Pytest, unittest, nose2
Runners: pytest, tox, nox
Coverage: coverage.py, pytest-cov

框架：Pytest、unittest、nose2
运行器：pytest、tox、nox
覆盖率工具：coverage.py、pytest-cov

Java

Frameworks: JUnit 5, TestNG, Mockito
Runners: Maven Surefire, Gradle Test
Coverage: JaCoCo, Cobertura

框架：JUnit 5、TestNG、Mockito
运行器：Maven Surefire、Gradle Test
覆盖率工具：JaCoCo、Cobertura

Limitations

局限性

Scope

范围限制

Unit Tests Focus: Primarily optimized for unit tests (integration tests require different patterns)
Static Analysis Only: Cannot execute tests or measure actual code behavior
Language Support: Best support for TypeScript, JavaScript, Python, Java (other languages limited)

聚焦单元测试：主要针对单元测试优化（集成测试需要不同的模式）
仅静态分析：无法执行测试或测量实际代码行为
语言支持：对TypeScript、JavaScript、Python、Java支持最佳（其他语言支持有限）

Coverage Analysis

覆盖率分析

Report Dependency: Requires existing coverage reports (cannot generate coverage from scratch)
Format Support: LCOV, JSON, XML only (other formats need conversion)
Interpretation Context: Coverage numbers need human judgment for meaningfulness

依赖现有报告：需要已有的覆盖率报告（无法从零生成覆盖率数据）
格式支持有限：仅支持LCOV、JSON、XML格式（其他格式需转换）
需人工解读：覆盖率数字需要结合人工判断才有实际意义

Test Generation

测试生成

Baseline Quality: Generated tests provide scaffolding, require human review and refinement
Complex Logic: Advanced business logic and integration scenarios need manual test design
Mocking Strategy: Mock/stub strategies should align with project patterns

基础质量保障：生成的测试仅提供脚手架，需要人工审核和完善
复杂逻辑限制：高级业务逻辑和集成场景需要手动设计测试
模拟策略对齐：模拟/桩函数策略需与项目现有模式保持一致

Framework Integration

框架集成

Configuration Required: Test runners need proper setup (this skill doesn't modify package.json or pom.xml)
Version Compatibility: Generated code targets recent stable versions (Jest 29+, Pytest 7+, JUnit 5+)

需自行配置：测试运行器需要正确设置（本技能不会修改package.json或pom.xml）
版本兼容性：生成的代码适配最新稳定版本（Jest 29+、Pytest 7+、JUnit 5+）

When NOT to Use This Skill

不适用场景

E2E Testing: Use dedicated E2E tools (Playwright, Cypress, Selenium)
Performance Testing: Use JMeter, k6, or Locust
Security Testing: Use OWASP ZAP, Burp Suite, or security-focused tools
Manual Testing: Some scenarios require human exploratory testing

端到端测试：使用专用的E2E工具（Playwright、Cypress、Selenium）
性能测试：使用JMeter、k6或Locust
安全测试：使用OWASP ZAP、Burp Suite或专注安全的工具
手动测试：部分场景需要人工探索性测试

Example Workflows

示例工作流

Workflow 1: Generate Tests from Requirements

工作流1：从需求生成测试

Input: User story + API specification
Process: Parse requirements → Generate test cases → Create test stubs
Output: Complete test files ready for implementation

输入：用户故事 + API规范
流程：解析需求 → 生成测试用例 → 创建测试桩代码
输出：可直接用于实现的完整测试文件

Workflow 2: Improve Coverage

工作流2：提升覆盖率

Input: Coverage report + source code
Process: Identify gaps → Suggest tests → Generate test code
Output: Prioritized test cases for uncovered code

输入：覆盖率报告 + 源代码
流程：识别缺口 → 建议测试用例 → 生成测试代码
输出：针对未覆盖代码的优先级测试用例

Workflow 3: TDD New Feature

工作流3：TDD开发新功能

Input: Feature requirements
Process: Guide red-green-refactor → Validate each step → Suggest refactorings
Output: Well-tested feature with clean code

输入：功能需求
流程：引导红-绿-重构 → 验证每一步 → 提出重构建议
输出：测试完善、代码简洁的功能模块

Workflow 4: Framework Migration

工作流4：框架迁移

Input: Tests in Framework A
Process: Parse tests → Translate patterns → Generate equivalent tests
Output: Tests in Framework B with same coverage

输入：框架A中的测试代码
流程：解析测试 → 转换模式 → 生成等效测试
输出：框架B中的测试代码，保持相同覆盖率

Integration Points

集成场景

CI/CD Integration

CI/CD集成

Parse coverage reports from CI artifacts
Generate coverage badges and reports
Fail builds on coverage thresholds
Track coverage trends over time

从CI工件中解析覆盖率报告
生成覆盖率徽章和报告
当覆盖率低于阈值时触发构建失败
跟踪覆盖率随时间的变化趋势

IDE Integration

IDE集成

Generate tests for selected code
Run coverage analysis on save
Highlight untested code paths
Quick-fix suggestions for test gaps

为选中的代码生成测试
保存时自动运行覆盖率分析
高亮未测试的代码路径
针对测试缺口提供快速修复建议

Code Review

代码评审

Validate test coverage in PRs
Check test quality standards
Identify missing test scenarios
Suggest improvements before merge

在PR中验证测试覆盖率
检查测试质量标准
识别缺失的测试场景
合并前提出改进建议

Version Support

版本支持

Node.js: 16+ (Jest 29+, Vitest 0.34+)
Python: 3.8+ (Pytest 7+)
Java: 11+ (JUnit 5.9+)
TypeScript: 4.5+

Node.js：16+（适配Jest 29+、Vitest 0.34+）
Python：3.8+（适配Pytest 7+）
Java：11+（适配JUnit 5.9+）
TypeScript：4.5+