Project 6: Pong Vertical Slice

A polished Pong build with menus, pause, settings, and local two player mode.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate Weekend
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 2
Business Potential Level 2
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Game loop composition, UI to gameplay boundaries, Playtesting loops

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)

Game loop composition

Fundamentals Game loop composition 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 Game loop composition 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.

UI to gameplay boundaries

Fundamentals UI to gameplay boundaries ensures the project scales from local prototype behavior to repeatable system behavior.

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

Playtesting loops

Fundamentals Playtesting loops 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 Playtesting loops 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 complete Pong vertical slice including menus, gameplay loop, pause, scoring, and end-of-match flow.

Visible game deliverable:

  • Main menu, settings panel, and in-match scoreboard
  • Arena with two paddles, ball trail, and score cap indicator
  • Match-end screen with winner and rematch action

3.2 Functional Requirements

  1. Implement full scene flow: menu -> gameplay -> end screen.
  2. Handle paddle movement, ball collisions, and scoring rules.
  3. Provide pause/resume and reset match states.
  4. Persist basic settings (volume, controls) between runs.

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

[PONG] match_start score_cap=10
[PONG] score P1=10 P2=7 winner=P1
[UI] end_screen shown rematch_available=true

3.5 Data Formats / Schemas / Protocols

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

3.6 Edge Cases

  • Ball stuck in vertical bounce loop.
  • Pause during scoring event.
  • Settings changes during active match.

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:

  • Main menu, settings panel, and in-match scoreboard
  • Arena with two paddles, ball trail, and score cap indicator
  • Match-end screen with winner and rematch action

Project 6 Pong Vertical Slice Window Mockup

How you interact:

  • W/S for Player 1
  • Up/Down for Player 2
  • Esc pause

3.7.1 How to Run (Copy/Paste)

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

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 pong
[PONG] match_start score_cap=10
[PONG] score P1=10 P2=7 winner=P1
[UI] end_screen shown rematch_available=true

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| pong                                   [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

Menu Scene -> Match Scene -> Score System -> End Scene -> Rematch Loop

Menu Scene -> Match Scene -> Score System -> End Scene -> Rematch Loop

4.2 Key Components

Component Responsibility Key Decisions
MatchSceneController Coordinates round state and transitions Explicit phase states prevent soft locks
BallPhysics Handles paddle/wall collisions and serve resets Deterministic serve and speed ramping
UISceneFlow Navigates menu/pause/end screens Single route table for scene transitions

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

“What minimum set of systems turns a prototype into a shippable tiny game?”

5.4 Concepts You Must Understand First

  1. Game loop composition
  2. UI to gameplay boundaries
  3. Playtesting loops

5.5 Questions to Guide Your Design

  1. What state machine prevents scene-transition bugs?
  2. How do you guarantee score events are idempotent?
  3. Which match metrics should be surfaced to debug pacing?

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 “The Art of Game Design by Jesse Schell” 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.