Project 7: Breakout with Power-Ups

A Breakout clone with deterministic brick layouts, drop tables, and progression pacing.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate 1 week
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 3
Business Potential Level 2
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Entity lifecycle rules, Spawn probability tables, Difficulty ramp curves

1. Learning Objectives

  1. Translate one concrete production question into a testable implementation plan.
  2. Implement and validate the feature in a MonoGame runtime context.
  3. Instrument success and failure paths with actionable diagnostics.
  4. Produce a repeatable demo artifact for portfolio or interview use.

2. All Theory Needed (Per-Concept Breakdown)

Entity lifecycle rules

Fundamentals Entity lifecycle rules is central to this project because it defines the non-negotiable behavioral contract for the feature. You should be able to describe valid inputs, legal state transitions, and expected outputs under normal and failure conditions.

Deep Dive into the concept Treat Entity lifecycle rules as a boundary-setting mechanism. Start by defining the smallest deterministic scenario that proves the feature works. Stress that scenario under altered timing, altered content inputs, and altered user actions. If behavior changes unexpectedly, document hidden coupling and sequence assumptions. Keep transitions explicit and observable via logs or debug panels. Connect each transition to an event record so regression analysis is possible after refactors.

Spawn probability tables

Fundamentals Spawn probability tables ensures the project scales from local prototype behavior to repeatable system behavior.

Deep Dive into the concept Use Spawn probability tables to reason about data flow ownership and mutation timing. Document where writes occur, when validation runs, and how rollback behaves if a write fails.

Difficulty ramp curves

Fundamentals Difficulty ramp curves connects this project to shipping reality by forcing you to think about operational constraints early.

Deep Dive into the concept Define one production-like failure mode related to Difficulty ramp curves and build a mitigation checklist. The solution is complete when you can demonstrate both a golden path and a controlled failure path.

3. Project Specification

3.1 What You Will Build

A Breakout game module with seeded level layouts, power-up drop tables, combo scoring, and progression states.

Visible game deliverable:

  • Brick grid with health variants and drop indicators
  • HUD with score, lives, combo multiplier, active power-ups
  • Level-clear banner and transition animation

3.2 Functional Requirements

  1. Load seeded brick layouts for deterministic level runs.
  2. Implement power-up drops with configurable probabilities.
  3. Support multi-ball and paddle modifiers with timers.
  4. Handle level clear, life loss, and game-over transitions cleanly.

3.3 Non-Functional Requirements

  • Performance: Must remain inside project-appropriate frame budget.
  • Reliability: Must recover from at least one injected failure mode.
  • Usability: Outcome must be observable by a reviewer in under two minutes.

3.4 Example Usage / Output

[BRK] level=1 seed=101 bricks=84
[BRK] drop=WidePaddle spawned=true duration=12s
[BRK] level_clear next_level=2

3.5 Data Formats / Schemas / Protocols

  • Event record: {timestamp, module, action, result}
  • Feature state snapshot: {version, state, counters, flags}

3.6 Edge Cases

  • Multi-ball with simultaneous out-of-bounds events.
  • Power-up pickup at timer expiry boundary.
  • Ball trapped in repetitive loop trajectories.

3.7 Real World Outcome

This is a game-facing outcome you can see and play immediately.

What you will see in the game window:

  • Brick grid with health variants and drop indicators
  • HUD with score, lives, combo multiplier, active power-ups
  • Level-clear banner and transition animation

Project 7 Breakout with Power-Ups Window Mockup

How you interact:

  • Left/Right move paddle
  • Space launch ball
  • Esc pause

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene breakout

3.7.2 Golden Path Demo (Deterministic)

  1. Start the scene and confirm all HUD panels load.
  2. Perform the three core interactions listed above.
  3. Verify the success signal appears without warnings.

3.7.3 If CLI: exact transcript

$ dotnet run --project src/Game -- --scene breakout
[BRK] level=1 seed=101 bricks=84
[BRK] drop=WidePaddle spawned=true duration=12s
[BRK] level_clear next_level=2

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| breakout                                   [F1 HUD] |
|------------------------------------------------------|
| PLAYFIELD: gameplay objects and interactions         |
| HUD: key metrics + status badges                    |
| STATUS: success/failure cues and prompts            |
+------------------------------------------------------+

4. Solution Architecture

4.1 High-Level Design

Level Loader -> Paddle/Ball Simulation -> Brick System -> Power-Up System -> Progression Manager

Level Loader -> Paddle/Ball Simulation -> Brick System -> Power-Up System -> Progression Manager

4.2 Key Components

Component Responsibility Key Decisions
BrickField Maintains brick states and hit responses Seeded generation for reproducible layouts
PowerUpController Spawns and applies timed modifiers Table-driven probability and duration settings
ProgressionState Controls level/life/game-over transitions No implicit transitions from gameplay events

4.4 Algorithm Overview

  1. Validate preconditions.
  2. Apply deterministic transition.
  3. Emit feedback and telemetry.
  4. Persist if required.

5. Implementation Guide

5.3 The Core Question You’re Answering

“How do you add systemic depth without breaking game readability?”

5.4 Concepts You Must Understand First

  1. Entity lifecycle rules
  2. Spawn probability tables
  3. Difficulty ramp curves

5.5 Questions to Guide Your Design

  1. How will you keep power-up chaos readable?
  2. What seeded data is required for reproducible level tests?
  3. How will you prevent progression soft locks after edge-case deaths?

5.6 Thinking Exercise

Trace one full success path and one failure path on paper before implementation.

5.7 The Interview Questions They’ll Ask

  1. Why did you pick this architecture boundary?
  2. Which failure mode did you prioritize first and why?
  3. How does your instrumentation accelerate debugging?
  4. How would you scale this feature to a larger game?

5.8 Hints in Layers

  • Hint 1: Stabilize one invariant before feature expansion.
  • Hint 2: Add diagnostics before optimization.
  • Hint 3: Keep platform calls at system boundaries.
  • Hint 4: Re-run deterministic scenario after each refactor.

5.9 Books That Will Help

Topic Book Chapter
Core concept “Game Programming Patterns by Robert Nystrom” Relevant concept chapter
Reliability “Release It!” Failure handling chapters
Architecture “Clean Architecture” Boundary and dependency chapters

6. Testing Strategy

  1. Golden path completes and emits success signal.
  2. Injected failure path recovers without crash.
  3. Re-run scenario after restart and confirm consistency.

7. Common Pitfalls & Debugging

  • Hidden initialization order coupling
  • Time-coupled behavior tied to render rate
  • Missing fallback behavior on platform call failure

8. Extensions & Challenges

  • Beginner: add one extra diagnostics panel metric.
  • Intermediate: add replay capture for event flow.
  • Advanced: add automated stress test harness.

9. Real-World Connections

This project mirrors shipping feature-module work in real indie and mid-size game teams.

10. Resources

  • Steamworks official docs
  • MonoGame docs
  • Gemini image generation docs (for asset-related projects)

11. Self-Assessment Checklist

  • I can explain the feature invariant and prove it in a demo.
  • I can trigger and handle one deterministic failure scenario.
  • I can describe tradeoffs and future scaling choices.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Feature works in deterministic golden path.
  • One controlled failure path is handled gracefully.
  • Core diagnostics are visible and documented.

Full Completion:

  • All minimum criteria plus edge-case coverage and regression checks.

Excellence:

  • Includes polished instrumentation and clear productionization notes.