Introduction to Refactoring: Goals and Process

Overview

Welcome to the first lecture of Section 10: Mastering Refactoring in the Official CTO journey! Refactoring is the process of improving code structure without changing its behavior, reducing complexity and enhancing maintainability. In this 15-minute lesson, we explore the goals and process of refactoring, focusing on test-driven refactoring to ensure correctness, using a recommendation system example. Drawing from my 8+ years of mentoring engineers, this lecture equips you to refactor codebases for FAANG interviews and real-world projects. Let’s kick off your Official CTO journey in Section 10!

Inspired by Refactoring by Martin Fowler, Code Complete 2 (Chapter 24), and Refactoring.Guru, this lesson provides actionable insights and a practical example.

Learning Objectives

• Understand refactoring goals: improve structure, preserve behavior.
• Learn the test-driven refactoring process to ensure correctness.
• Prepare for FAANG interviews with refactoring-focused questions.
• Apply refactoring in a recommendation system example.

Why Refactoring Matters

Refactoring reduces technical debt, improves code quality, and enhances collaboration, making it a critical skill for FAANG interviews. Drawing from my experience mentoring engineers, I’ve seen refactoring expertise distinguish candidates in code reviews and leadership roles. This lecture ensures you can refactor effectively, articulate improvements, and align with industry standards.

In software engineering, refactoring helps you:

• Ace Interviews: Demonstrate code quality in refactoring tasks.
• Reduce Technical Debt: Simplify maintenance and prevent rewrites.
• Enhance Collaboration: Improve code clarity for teams.
• Improve Scalability: Build maintainable, extensible systems.

Key Concepts

1. Refactoring Goals

• Definition: Improving code structure without altering external behavior (Refactoring by Fowler, Code Complete 2 Ch. 24).
• Goals: Enhance readability, maintainability, testability, and reduce complexity.
• Example: Simplify a recommendation system to reduce cyclomatic complexity.

2. Test-Driven Refactoring

• Definition: Refactor with unit tests to ensure behavior preservation.
• Process: Write tests, refactor incrementally, verify tests pass.
• Benefits: Prevents regressions, builds confidence in changes.
• Example: Refactor a recommendation algorithm with tests.

3. Role in FAANG Interviews

• Technical questions test refactoring skills (e.g., “Refactor this code to reduce complexity”).
• Behavioral questions assess experience (e.g., “Tell me about a time you refactored a system”).
• Align with company priorities (e.g., Google’s clarity, Amazon’s ownership).

4. Relation to Previous Sections

• Algorithms (Section 1): Refactoring clarifies algorithmic logic.
• OOD (Section 2): Aligns with cohesive class design.
• Design Patterns (Section 3): Patterns support refactoring.
• Design Principles (Section 4): SOLID drives refactoring practices.
• HLD/LLD (Sections 5–6): Refactoring supports system design (e.g., Mock LLD Interview, Lecture 31).
• Behavioral Skills (Section 7): Articulating refactoring builds on communication (Lecture 2).
• Domain-Specific Topics (Section 8): Refactoring ensures maintainable cloud systems (e.g., microservices, Lecture 7).
• Clean Code (Section 9): Builds on readability, modularity, error handling, testability, documentation.

Code Example: Refactoring a Recommendation System

Below is a Java example showing a poorly written recommendation system, followed by its refactored version with unit tests to ensure behavior preservation.

Before Refactoring

public class RecommendationSystem {
    public List<String> getRecs(long id, List<String> items, Map<String, Double> scores) {
        List<String> result = new ArrayList<>();
        for (String item : items) {
            if (scores.containsKey(item)) {
                if (scores.get(item) > 0.5) {
                    if (id > 0) {
                        result.add(item);
                        if (result.size() >= 5) {
                            break;
                        }
                    }
                }
            }
        }
        return result;
    }
}

After Refactoring

/**
 * Manages product recommendations based on user preferences.
 */
public class RecommendationSystem {
    private final ProductScorer productScorer;

    public RecommendationSystem(ProductScorer productScorer) {
        this.productScorer = productScorer;
    }

    /**
     * Retrieves top recommendations for a user, limited to maxRecommendations.
     *
     * @param userId User identifier
     * @param products List of product IDs
     * @param maxRecommendations Maximum number of recommendations
     * @return List of recommended product IDs
     */
    public List<String> getRecommendations(long userId, List<String> products, int maxRecommendations) {
        if (userId <= 0 || products == null || maxRecommendations <= 0) {
            return Collections.emptyList();
        }

        List<ProductScore> scoredProducts = scoreProducts(products, userId);
        return selectTopRecommendations(scoredProducts, maxRecommendations);
    }

    private List<ProductScore> scoreProducts(List<String> products, long userId) {
        List<ProductScore> scoredProducts = new ArrayList<>();
        for (String product : products) {
            double score = productScorer.getScore(userId, product);
            if (score > 0.5) {
                scoredProducts.add(new ProductScore(product, score));
            }
        }
        return scoredProducts;
    }

    private List<String> selectTopRecommendations(List<ProductScore> scoredProducts, int maxRecommendations) {
        scoredProducts.sort((a, b) -> Double.compare(b.score, a.score));
        List<String> recommendations = new ArrayList<>();
        for (int i = 0; i < maxRecommendations && i < scoredProducts.size(); i++) {
            recommendations.add(scoredProducts.get(i).productId);
        }
        return recommendations;
    }
}

class ProductScore {
    final String productId;
    final double score;

    ProductScore(String productId, double score) {
        this.productId = productId;
        this.score = score;
    }
}

interface ProductScorer {
    double getScore(long userId, String productId);
}

Unit Tests

import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.*;
import static org.mockito.Mockito.*;

class RecommendationSystemTest {
    private ProductScorer productScorer;
    private RecommendationSystem system;

    @BeforeEach
    void setUp() {
        productScorer = mock(ProductScorer.class);
        system = new RecommendationSystem(productScorer);
    }

    @Test
    void testGetRecommendations_ValidInput() {
        List<String> products = Arrays.asList("item1", "item2", "item3");
        when(productScorer.getScore(1L, "item1")).thenReturn(0.8);
        when(productScorer.getScore(1L, "item2")).thenReturn(0.6);
        when(productScorer.getScore(1L, "item3")).thenReturn(0.4);

        List<String> result = system.getRecommendations(1L, products, 2);

        assertEquals(Arrays.asList("item1", "item2"), result);
        verify(productScorer, times(3)).getScore(anyLong(), anyString());
    }

    @Test
    void testGetRecommendations_InvalidUserId() {
        List<String> result = system.getRecommendations(0L, Arrays.asList("item1"), 2);

        assertTrue(result.isEmpty());
        verifyNoInteractions(productScorer);
    }
}

• Explanation:
  • Before Refactoring: The original code has a Long Method smell, nested conditionals, and unclear intent.
  • After Refactoring: Applies Extract Method (split into scoreProducts and selectTopRecommendations), introduces dependency injection (ProductScorer), and uses clear names (Refactoring by Fowler).
  • Test-Driven: Unit tests ensure behavior preservation, covering valid and invalid inputs (Code Complete 2 Ch. 24).
  • Improvements: Reduces complexity, enhances readability, and improves testability.
• Setup:
  • Add dependencies: org.junit.jupiter:junit-jupiter, org.mockito:mockito-core.
  • Run tests with mvn test or an IDE.
• Big O: O(n) for scoring (n = products), O(n log n) for sorting in selectTopRecommendations.
• Edge Cases: Handles invalid user IDs, null products, and zero maxRecommendations.
• Trade-Offs: Dependency injection for testability vs. simpler logic; modular methods for clarity vs. monolithic function.

FAANG-Specific Tips

• Amazon (Ownership):
  • Highlight ownership in refactoring (e.g., “I took ownership of simplifying code”).
  • Emphasize reliability (e.g., “Ensured robust recommendations”).
  • STAR Response:
    • Situation: “Our recommendation system was complex and unmaintainable.”
    • Task: “I was responsible for refactoring.”
    • Action: “I extracted methods, added tests, and ensured behavior preservation.”
    • Result: “Reduced complexity by 30%, improving reliability.”
• Google (Clarity):
  • Focus on Google’s Java Style Guide (e.g., “I refactored with clear naming”).
  • Emphasize collaboration (e.g., “Aligned with team on refactoring”).
  • STAR Response:
    • Situation: “Our system had unclear, complex code.”
    • Task: “I was tasked with refactoring.”
    • Action: “I applied Google’s style guide, extracted methods, and collaborated on reviews.”
    • Result: “Improved clarity, praised by team.”
• Meta (Execution Speed):
  • Highlight rapid refactoring (e.g., “I refactored in a sprint”).
  • Focus on maintainability (e.g., “Enabled faster updates”).
  • STAR Response:
    • Situation: “Our recommendation system slowed development.”
    • Task: “I was responsible for refactoring.”
    • Action: “I quickly extracted methods and added tests for maintainability.”
    • Result: “Reduced maintenance time by 25%.”
• Netflix (Freedom & Responsibility):
  • Emphasize autonomous refactoring (e.g., “I independently refactored code”).
  • Focus on high-impact outcomes (e.g., “Improved system maintainability”).
  • STAR Response:
    • Situation: “Our system had complex recommendation logic.”
    • Task: “I was responsible for improvements.”
    • Action: “I independently refactored with test-driven methods.”
    • Result: “Reduced bugs by 30%, boosting maintainability.”

Practice Exercise

Problem: Refactor a poorly written recommendation function to improve structure and reduce complexity.

1. Define Requirements:
   • Simplify a complex function using Extract Method or similar techniques.
   • Ensure behavior preservation with unit tests.
2. Craft a STAR Response:
   • Situation: Describe a project with complex code (e.g., recommendation system).
   • Task: Clarify your role (e.g., code refactorer).
   • Action: List 2–3 actions (e.g., extracted methods, wrote tests).
   • Result: Quantify outcomes (e.g., reduced complexity, improved maintainability).
3. Write Refactored Code:
   • Refactor a Java function to be modular and testable.
   • Write unit tests to verify behavior.
   • Test with mvn test.
4. Tailor to a FAANG Company:
   • Align with Amazon (Ownership), Google (Clarity), Meta (speed), or Netflix (autonomy).
5. Write and Review:
   • Write a 100–150 word STAR response.
   • Ensure clarity, specificity, and alignment with refactoring principles.

Sample Response (Amazon - Ownership):

• Situation: “Our recommendation system had a complex, monolithic function.”
• Task: “As lead, I was responsible for improving it.”
• Action: “I extracted methods, used dependency injection, and wrote tests to ensure correctness.”
• Result: “Reduced complexity by 30%, improving system reliability.”

Conclusion

Mastering refactoring goals and processes equips you to excel in FAANG interviews and build maintainable systems. This lecture kicks off Section 10, building on clean code from Section 9, and advances your Official CTO journey.

Next Step: Explore Identifying Code Smells or revisit all sections.

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