Lab Guides Summary and Resources

This document provides cross-cutting analysis and tools to help you get the most out of the lab project guides. Use these resources to deepen your understanding of problem-solving patterns and collaborate more effectively with peers.

Problem-Solving Strategy Comparison Across Labs

Overview Matrix

Detailed Strategy Analysis

PhoneBook: Mapping & Consistency

Problem Type: Data storage with bidirectional access Strategic Approach:

• Start with single direction (name→number)
• Add reverse direction with synchronization
• Handle edge cases (duplicates, not found)
• Optimize for lookup speed

Key Insight: Abstraction hides implementation complexity while providing clean interface

Transferable Skills:

• Database design principles
• Cache management strategies
• API design consistency

DiceyLab: Simulation & Statistics

Problem Type: Statistical modeling through simulation Strategic Approach:

• Model individual events (single die roll)
• Compose complex events (multiple dice)
• Collect and analyze results
• Verify against mathematical expectations

Key Insight: Breaking complex probability into simple, composable events

Transferable Skills:

• Monte Carlo simulation techniques
• Test-driven development
• Statistical validation methods

Hamurabi: State Management & User Interaction

Problem Type: Interactive simulation with persistent state Strategic Approach:

• Model game state as data structure
• Separate input/output from game logic
• Handle invalid user inputs gracefully
• Track state changes over time

Key Insight: Separating presentation from business logic enables testing and flexibility

Transferable Skills:

• MVC pattern implementation
• User input validation
• State machine design

Cross-Lab Patterns

Progressive Complexity Strategy

All three labs benefit from the same development approach:

1. Minimal Viable Product (MVP) - Core functionality only
2. Edge Case Handling - Deal with invalid inputs and error conditions
3. Enhancement Phase - Add usability and advanced features
4. Optimization Phase - Improve performance and user experience

Testing Philosophy

Each lab teaches different aspects of testing:

• PhoneBook: Unit testing with state verification
• DiceyLab: Statistical testing with large sample sizes
• Hamurabi: Integration testing with user interaction scenarios

Abstraction Levels

• PhoneBook: Data structure abstraction
• DiceyLab: Mathematical model abstraction
• Hamurabi: Game logic abstraction

Peer Programming Checklist

Use this checklist when reviewing a classmate’s approach or getting feedback on your own work.

Pre-Implementation Review

Problem Understanding

• Can you explain the real-world problem in your own words?
• Have you identified all the core operations needed?
• Do you understand what data needs to be stored and how?
• Have you considered the most challenging edge cases?

Design Decisions

• Can you justify your choice of data structures?
• Have you considered alternative approaches?
• Is your class/method design focused and cohesive?
• Will your design be easy to test?

Planning

• Do you have a clear implementation order (phases)?
• Have you thought about how to test each component?
• Do you know what success looks like for each phase?

Implementation Review

Code Quality

• Are method and variable names clear and descriptive?
• Is each method doing exactly one thing?
• Are the methods short enough to understand quickly?
• Is the code readable without extensive comments?

Error Handling

• What happens with invalid inputs?
• Are error messages helpful to users?
• Does the code fail gracefully or crash?
• Are edge cases handled consistently?

Testing Coverage

• Does each public method have at least one test?
• Are both normal cases and edge cases tested?
• Do tests verify the right behavior, not just “no crash”?
• Would tests catch common mistakes?

Post-Implementation Review

Design Reflection

• What would you change if you started over?
• Which parts were harder than expected? Why?
• What assumptions turned out to be wrong?
• How well does your solution handle unexpected inputs?

Extension Readiness

• How hard would it be to add a new feature?
• Could someone else understand and modify your code?
• What would break if the requirements changed slightly?
• Are there obvious performance improvements?

Peer Review Questions

For the Code Author

1. “Walk me through your design decisions - why did you choose this approach?”
2. “What was the hardest part to implement and how did you solve it?”
3. “What would you do differently if you started over?”
4. “How confident are you that your solution handles edge cases?”

For the Reviewer

1. “What do you like about this approach?”
2. “What alternative approaches can you think of?”
3. “What potential problems do you see?”
4. “What questions does this code raise for you?”

Collaborative Discussion

1. “Let’s trace through this code with a tricky input…”
2. “What if the requirements changed to include…?”
3. “How would you test this functionality?”
4. “What real-world systems might work similarly?”

Common Patterns Guide

Recurring Design Patterns Across All Labs

Pattern 1: Encapsulation with Hidden Complexity

What It Is: Providing a simple interface that hides complex internal operations

PhoneBook Example:

// Simple interface
phonebook.add("John", "555-1234")
String number = phonebook.lookup("John")

// Hidden complexity: maintaining two internal data structures

DiceyLab Example:

// Simple interface
Dice dice = new Dice(6, 2)  // 2 six-sided dice
int result = dice.roll()

// Hidden complexity: random number generation and summation

Hamurabi Example:

// Simple interface
game.buyLand(100)

// Hidden complexity: validating funds, updating multiple state variables

Why It Matters: Users don’t need to understand implementation details to use your classes effectively.

Pattern 2: State Consistency Management

What It Is: Ensuring that related data stays synchronized when changes occur

PhoneBook Example:

• When adding entry, both name→number and number→name maps must be updated
• When removing entry, both maps must be cleaned up

DiceyLab Example:

• When rolling multiple dice, individual results must sum correctly
• When tracking statistics, counts must match total rolls

Hamurabi Example:

• When buying land, grain stores must decrease and land holdings increase
• Population changes must affect labor capacity

Why It Matters: Inconsistent state leads to bugs that are hard to track down.

Pattern 3: Input Validation and Error Handling

What It Is: Checking inputs before processing and handling problems gracefully

Common Validation Patterns:

• Null/Empty Checks: Don’t process empty or null inputs
• Range Validation: Ensure numbers are within acceptable bounds
• Format Validation: Check that strings match expected patterns
• Business Logic Validation: Ensure operations make sense in context

Error Handling Strategies:

• Return Special Values: null, empty string, -1 for “not found”
• Throw Exceptions: For serious errors that caller must handle
• Log and Continue: For non-critical issues
• Ask User Again: For interactive programs with bad input

Pattern 4: Incremental Development

What It Is: Building software in stages, testing each stage before moving on

Universal Phases:

1. Core Data Model: Get basic storage working
2. Primary Operations: Implement main functionality
3. Edge Case Handling: Deal with invalid/unusual inputs
4. User Experience: Add convenience features and polish

Benefits:

• Always have something working
• Easier to debug problems
• Can stop at any phase if time runs out
• Builds confidence through small successes

Pattern 5: Separation of Concerns

What It Is: Keeping different types of logic in different places

Common Separations:

• Data vs. Behavior: Store data in fields, operations in methods
• Input/Output vs. Logic: User interaction separate from business rules
• Validation vs. Processing: Check inputs before doing work
• Testing vs. Implementation: Test code separate from production code

PhoneBook Example:

• Storage logic separate from lookup logic
• Validation separate from data manipulation

DiceyLab Example:

• Random number generation separate from statistics collection
• Individual die behavior separate from multiple dice behavior

Hamurabi Example:

• Game state separate from user interface
• Turn logic separate from input/output

Architectural Patterns That Emerge

The Model-View Pattern

What You’re Learning: All three labs teach you to separate data/logic (Model) from user interaction (View)

PhoneBook: PhoneBook class (model) separate from main method that interacts with user (view) DiceyLab: Dice classes (model) separate from code that displays results (view) Hamurabi: Game state and rules (model) separate from input/output code (view)

Why This Matters: This separation is fundamental to all GUI applications, web development, and mobile apps.

The Strategy Pattern

What You’re Learning: Different ways to solve the same problem, with trade-offs between them

PhoneBook: Different ways to handle reverse lookup (dual maps vs. search) DiceyLab: Different ways to generate random numbers (built-in vs. custom) Hamurabi: Different ways to handle invalid input (re-prompt vs. default values)

Why This Matters: Professional development involves constantly choosing between alternative approaches.

The Builder Pattern

What You’re Learning: Constructing complex objects step by step

PhoneBook: Building up contact database entry by entry DiceyLab: Accumulating statistics roll by roll Hamurabi: Progressing through game state turn by turn

Why This Matters: Many systems need to construct complex state gradually rather than all at once.

Meta-Patterns: Learning How to Learn

Pattern Recognition

• Seeing similarities between different problems
• Applying solutions from one domain to another
• Building a toolkit of reliable approaches

Iterative Refinement

• Starting with simple solutions
• Gradually adding complexity
• Refactoring when better approaches become clear

Systematic Debugging

• Isolating problems to specific components
• Testing assumptions with simple cases
• Using print statements or debugger effectively

Design Documentation

• Explaining design decisions to others
• Documenting assumptions and trade-offs
• Creating examples that illustrate usage

Connecting the Dots: From Labs to Industry

Database Design (PhoneBook Skills)

• Indexing: Your dual-map approach is like database indexes
• Normalization: Avoiding duplicate data
• Query Optimization: Fast lookups vs. memory usage

Statistical Computing (DiceyLab Skills)

• Monte Carlo Methods: Using simulation to solve complex problems
• A/B Testing: Comparing different approaches statistically
• Machine Learning: Training models on large datasets

Interactive Systems (Hamurabi Skills)

• Game Development: Managing game state and user interaction
• Web Applications: Handling user requests and maintaining session state
• Mobile Apps: Responding to user input while maintaining app state

Universal Skills from All Labs

• API Design: Creating clean, intuitive interfaces
• Error Handling: Graceful degradation and user-friendly error messages
• Testing: Ensuring code works correctly under various conditions
• Documentation: Making code understandable to future developers

Remember: These labs aren’t just about solving specific problems—they’re about developing the thinking patterns that make you effective at solving any programming problem!