Project 3: Input Mapping and Command Buffer

An action based input system with keyboard and gamepad rebinding and temporal input buffering.

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 1
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Action abstraction, Edge triggered states, Input buffering

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)

Action abstraction

Fundamentals Action abstraction 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 Action abstraction 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.

Edge triggered states

Fundamentals Edge triggered states ensures the project scales from local prototype behavior to repeatable system behavior.

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

Input buffering

Fundamentals Input buffering 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 Input buffering 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 action-mapping input system for keyboard/gamepad with edge detection, runtime rebinding, and command-buffered combo detection.

Visible game deliverable:

  • Action state table shows Pressed/Held/Released transitions per frame
  • Input history strip shows last 20 actions with timestamps
  • Combo detector panel highlights matched command sequences

3.2 Functional Requirements

  1. Track per-frame action edges (pressed/held/released).
  2. Allow runtime remapping and persistence of action bindings.
  3. Maintain command buffer with timestamped actions and expiry windows.
  4. Support keyboard and gamepad profiles with unified action layer.

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

[INPUT] action=Jump state=Pressed
[INPUT] rebind Jump -> J saved=true
[BUFFER] sequence=Right,Right,Down,Attack match=PASS

3.5 Data Formats / Schemas / Protocols

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

3.6 Edge Cases

  • Device disconnect/reconnect mid-session.
  • Simultaneous keyboard and gamepad input conflicts.
  • Buffer timing windows near threshold boundaries.

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:

  • Action state table shows Pressed/Held/Released transitions per frame
  • Input history strip shows last 20 actions with timestamps
  • Combo detector panel highlights matched command sequences

Project 3 Input Mapping and Command Buffer Window Mockup

How you interact:

  • R enters rebinding mode
  • Arrow keys and action keys feed command buffer
  • F2 toggles raw device vs action abstraction view

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene input-lab

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 input-lab
[INPUT] action=Jump state=Pressed
[INPUT] rebind Jump -> J saved=true
[BUFFER] sequence=Right,Right,Down,Attack match=PASS

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| input-lab                                   [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

Raw Device Poll -> Action Mapper -> Edge Detector -> Command Buffer -> Gameplay Intent

Raw Device Poll -> Action Mapper -> Edge Detector -> Command Buffer -> Gameplay Intent

4.2 Key Components

Component Responsibility Key Decisions
InputDeviceAdapter Normalizes keyboard/gamepad raw states Separate device polling from gameplay actions
ActionMap Maps physical inputs to logical actions Runtime-reloadable binding profiles
CommandBuffer Stores timed action history for combos Deterministic expiry and matching rules

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 make controls feel responsive and rebindable without spaghetti code?”

5.4 Concepts You Must Understand First

  1. Action abstraction
  2. Edge triggered states
  3. Input buffering

5.5 Questions to Guide Your Design

  1. How will you avoid false positives in combo detection?
  2. What is your strategy for deterministic edge detection each frame?
  3. How should conflicting device inputs resolve at action layer?

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 Engine Architecture by Jason Gregory” 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.