Phase 3: Development Planning & Implementation

Overview

The Development phase leverages AI coding assistants to transform approved designs and requirements into working software. This phase emphasizes automation, quality guardrails, and maintaining high standards while dramatically increasing development velocity.

Process Flow

graph TD
    A[User Stories & Design] -->|AI Analysis| B[Implementation Plan]
    B -->|Review & Approval| C[Development]
    C -->|AI Assistance| D[Code Generation]
    D -->|Automated Tests| E[Quality Checks]
    E -->|Code Review| F[Pull Request]
    F -->|CI/CD Pipeline| G[Deployment Ready]
    
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style G fill:#9f9,stroke:#333,stroke-width:2px

Step 1: AI-Powered Development Planning

Implementation Planning Process

Comprehensive Planning Prompt:

Analyze the following user story and create a detailed implementation plan:

[Insert User Story with Acceptance Criteria]

Include:
1. Architecture decisions and patterns to use
2. File structure and module organization
3. API endpoints and contracts
4. Data models and database changes
5. Test strategy (unit, integration, e2e)
6. Error handling approach
7. Security considerations
8. Performance optimizations
9. Documentation requirements

Consider:
- Existing codebase patterns in [repository]
- Technology stack: [list technologies]
- Coding standards: [link to standards]
- Non-functional requirements: [list]

Format as a structured plan with clear sections.

Plan Components

Component	Details	Validation
Architecture	Patterns, structure, dependencies	Tech lead review
API Design	Endpoints, payloads, responses	Contract testing
Data Models	Entities, relationships, migrations	Schema validation
Test Strategy	Coverage targets, test types	QA approval
Security	Auth, validation, encryption	Security scan

Step 2: Development Implementation

AI Coding Assistant Configuration

Context Setup:

# .claude-context or .github/copilot-context
   
Project: [Project Name]
Stack: TypeScript, React, Node.js, PostgreSQL
   
Patterns:
- Use functional components with hooks
- Implement error boundaries
- Follow REST conventions
- Use dependency injection
   
Standards:
- ESLint configuration: .eslintrc
- Prettier formatting: .prettierrc
- Test coverage minimum: 80%

Custom Instructions: ```markdown When generating code:
1. Follow existing patterns in the codebase
2. Include comprehensive error handling
3. Write tests alongside implementation
4. Add JSDoc comments for public APIs
5. Use meaningful variable names
6. Implement logging for debugging
7. Consider edge cases
8. Optimize for readability ```

Implementation Workflow

Feature Development:

// Example: AI-generated user service
import { Injectable } from '@nestjs/common';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
import { User } from './entities/user.entity';
import { CreateUserDto } from './dto/create-user.dto';
import { Logger } from '../common/logger';

@Injectable()
export class UserService {
  private readonly logger = new Logger(UserService.name);

  constructor(
    @InjectRepository(User)
    private userRepository: Repository<User>,
  ) {}

  async create(createUserDto: CreateUserDto): Promise<User> {
    this.logger.log('Creating new user', { email: createUserDto.email });
       
    try {
      const user = this.userRepository.create(createUserDto);
      const savedUser = await this.userRepository.save(user);
         
      this.logger.log('User created successfully', { userId: savedUser.id });
      return savedUser;
    } catch (error) {
      this.logger.error('Failed to create user', error);
      throw new Error('User creation failed');
    }
  }
}

Test Generation:

// AI-generated test suite
describe('UserService', () => {
  let service: UserService;
  let repository: MockRepository<User>;

  beforeEach(async () => {
    const module = await Test.createTestingModule({
      providers: [
        UserService,
        {
          provide: getRepositoryToken(User),
          useClass: MockRepository,
        },
      ],
    }).compile();

    service = module.get<UserService>(UserService);
    repository = module.get<MockRepository<User>>(getRepositoryToken(User));
  });

  describe('create', () => {
    it('should create a new user', async () => {
      const createUserDto: CreateUserDto = {
        email: 'test@example.com',
        name: 'Test User',
      };

      const savedUser = { id: '123', ...createUserDto };
      repository.create.mockReturnValue(savedUser);
      repository.save.mockResolvedValue(savedUser);

      const result = await service.create(createUserDto);

      expect(result).toEqual(savedUser);
      expect(repository.create).toHaveBeenCalledWith(createUserDto);
      expect(repository.save).toHaveBeenCalled();
    });

    it('should handle creation errors', async () => {
      repository.save.mockRejectedValue(new Error('Database error'));

      await expect(service.create(createUserDto))
        .rejects.toThrow('User creation failed');
    });
  });
});

Step 3: AI Guardrails Implementation

Comprehensive Guardrails Table

Job Name	Purpose	When Run	Tools Used
commit_message_validation	Validates conventional commit format and AI authorship	Commit Hook + CI/CD	commitlint, custom scripts
pr_message_validation	Validates comprehensive PR description	CI/CD	GitHub Actions, custom validators
lint	Enforces code style and quality standards	Pre-commit + CI/CD	ESLint, Prettier, language-specific linters
typecheck	Static type checking	Pre-commit + CI/CD	TypeScript, Flow, mypy
test_unit	Runs unit tests	Pre-commit + CI/CD	Jest, pytest, JUnit
test_integration	Runs integration tests	CI/CD	Jest, pytest, Postman
test_e2e	End-to-end browser tests	CI/CD	Playwright, Cypress, Selenium
visual_regression_test	Visual UI testing	CI/CD	Percy, Chromatic, BackstopJS
format	Code formatting consistency	Pre-commit + CI/CD	Prettier, Black, gofmt
build	Project compilation	CI/CD	Webpack, tsc, Maven
dependency_security_scan	Vulnerability scanning	CI/CD	Snyk, npm audit, OWASP
static_security_testing	SAST analysis	CI/CD	SonarQube, CodeQL, Checkmarx
secret_scanning	Detect hardcoded secrets	CI/CD	GitGuardian, TruffleHog
license_compliance	OSS license validation	CI/CD	FOSSA, WhiteSource
code_quality_check	AI-powered quality review	CI/CD	CodeClimate, DeepCode
compliance_validation	Framework compliance	CI/CD	Custom validators

Guardrail Configuration Example

# .github/workflows/guardrails.yml
name: AI Development Guardrails

on:
  pull_request:
    types: [opened, synchronize]
  push:
    branches: [main, develop]

jobs:
  quality-checks:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Validate Commit Messages
        run: |
          npm install -g @commitlint/cli
          commitlint --from=$
      
      - name: Run Linting
        run: |
          npm run lint
          npm run format:check
      
      - name: Type Checking
        run: npm run typecheck
      
      - name: Unit Tests
        run: npm run test:unit -- --coverage
      
      - name: Security Scanning
        uses: snyk/actions/node@master
        env:
          SNYK_TOKEN: $
      
      - name: SAST Analysis
        uses: github/super-linter@v4
        env:
          GITHUB_TOKEN: $

Step 4: Code Review Process

AI-Assisted Review

Automated PR Description:

## Summary
This PR implements user authentication functionality as specified in JIRA-123.
   
## Changes
- Added login endpoint with JWT token generation
- Implemented password hashing with bcrypt
- Created user session management
- Added comprehensive test coverage (92%)
   
## Testing
- [x] Unit tests passing
- [x] Integration tests passing
- [x] Manual testing completed
- [x] Security scan passed
   
## Checklist
- [x] Code follows project conventions
- [x] Tests cover edge cases
- [x] Documentation updated
- [x] No security vulnerabilities
   
🤖 Generated with AI assistance
Co-authored-by: AI Assistant <ai@example.com>

Review Focus Areas:
- Business logic correctness
- Security vulnerabilities
- Performance implications
- Code maintainability
- Test coverage

Human Review Guidelines

Review Aspect	What to Check	Red Flags
Business Logic	Correctness, edge cases	Assumptions, missing validation
Security	Auth, input validation	Hardcoded secrets, SQL injection
Performance	Queries, algorithms	N+1 queries, inefficient loops
Maintainability	Readability, patterns	Complex functions, poor naming
Testing	Coverage, scenarios	Missing edge cases, brittle tests

Step 5: Documentation Generation

Automated Documentation

Documentation Generation Prompt:

Generate comprehensive documentation for this code:
[Insert code]

Include:
Overview and purpose
API documentation with examples
Configuration options
Usage examples
Error handling
Performance considerations
Security notes
Troubleshooting guide

Documentation Types

API Documentation: ```typescript /**
- Creates a new user account
- @param {CreateUserDto} createUserDto - User creation data
- @returns {Promise} The created user object
- @throws {ConflictException} If email already exists
- @throws {BadRequestException} If validation fails
- @example
- const user = await userService.create({
- email: ‘user@example.com’,
- name: ‘John Doe’,
- password: ‘securePassword123’
- }); */ ```

README Updates:

## User Authentication Module
   
### Features
- JWT-based authentication
- Password hashing with bcrypt
- Session management
- Role-based access control
   
### Installation
```bash
npm install
npm run migration:run

Configuration

JWT_SECRET=your-secret-key
JWT_EXPIRATION=7d
BCRYPT_ROUNDS=10

```

Success Metrics

Development Velocity

Lines of Code per Day: 200-400 (150-200% increase)
Feature Completion: 160-140% faster
Bug Fix Time: 75-80% reduction
Code Review Time: 80-85% reduction

Code Quality

Test Coverage: 85-95%
Code Duplication: <5%
Cyclomatic Complexity: <10
Security Vulnerabilities: 0 critical/high

Documentation

API Coverage: 100%
Code Comments: All public methods
README Completeness: 95%
Update Frequency: With each PR

Best Practices

1. Context Maintenance

Keep AI context files updated
Document patterns and conventions
Share knowledge across team
Regular context reviews

2. Test-Driven Development

Write tests first or alongside code
Aim for high coverage
Test edge cases
Automate test execution

3. Security First

Input validation on all endpoints
Parameterized queries
Secure secret management
Regular dependency updates

4. Performance Optimization

Profile before optimizing
Use caching strategically
Optimize database queries
Monitor resource usage

Common Pitfalls & Solutions

Pitfall	Impact	Solution
Over-reliance on AI	Poor code quality	Human review critical
Ignoring context	Inconsistent code	Maintain context files
Skipping tests	Production bugs	Enforce coverage
Poor documentation	Maintenance issues	Automate generation
Security oversights	Vulnerabilities	Automated scanning

Tools & Resources

Recommended Stack

AI Coding: Cursor, GitHub Copilot ($20-39/user/month)
IDE: VS Code with AI extensions (Free)
Testing: Jest, Playwright ($0-500/month)
Security: Snyk, SonarQube ($32-98/month)

Templates & Examples

Next Steps

Configure AI Tools: Set up coding assistants
Implement Guardrails: Deploy CI/CD checks
Create Templates: Standardize patterns
Train Team: AI tool proficiency
Measure Impact: Track metrics

Continue to Phase 4: Quality Assurance & Deployment →

This phase typically increases development velocity by 150-200% while maintaining or improving code quality.