Benchmarking Context: Exploring AI Programming Boundaries in Game Development

System-level code generation faces three core challenges: implementing complex logic, ensuring cross-platform compatibility, and establishing robust error handling mechanisms. This study conducts two-phase comparative testing to evaluate next-gen LLMs’ capabilities in UEFI game development environments.

The test scenario involves developing Tetris in UEFI, covering:

• Graphics protocol initialization (GOP)
• Memory management (frame buffer operations)
• Event-driven architecture (keyboard input handling)
• Real-time rendering loop (game main cycle)

The testing framework combines intelligent programming tools against industry-standard solutions, documenting the complete workflow from code generation to issue resolution. Key evaluation metrics include code review accuracy, debug information analysis, and cross-module issue localization.

Phase 1 Testing: From Legacy Code to Functional Implementation

2.1 Code Migration and Initial Review

After transplanting seven modular code files (including entry functions, game logic, and rendering modules) from a legacy solution to a new development environment, the model conducted static analysis within 3 minutes, identifying:

1. Memory Management Flaws:
   - Missing frame buffer refresh after GOP protocol calls
   - Wild pointer risks from SetMode operations
2. Protocol Configuration Errors:
   - GUID mismatch with EDK2 standards
   - Incorrect graphics output protocol initialization sequence
3. Input Handling Issues:
   - Missing keyboard scan code conversion table
   - Event queue buffer overflow risks

2.2 Critical Issue Resolution

Memory Management Fix

The original code directly accessed Gop->Mode->FrameBufferBase after Gop->SetMode(), causing access violations. The solution implemented dynamic retrieval:

// Before (wild pointer risk)
UINTN FrameBufferBase = Gop->Mode->FrameBufferBase;
// After (safe retrieval)
EFI_GRAPHICS_OUTPUT_MODE_INFORMATION *Info;
UINTN InfoSize;
EFI_STATUS Status = Gop->QueryMode(Gop, Gop->Mode->Mode, &InfoSize, &Info);
if (EFI_ERROR(Status)) {
    // Error handling
}
UINTN FrameBufferBase = (UINTN)Info->FrameBufferBase;

Protocol Configuration Correction

GUID mismatches caused LocateProtocol() failures. The fix adopted standard EFI definitions:

// Incorrect configuration
#define MY_GOP_GUID \
    { 0x982c, 0xf9d8, { 0x45, 0xa0, { 0x95, 0x21, 0x2a, 0x47, 0x12, 0x34 } } }
// Correct implementation
#include <Protocol/GraphicsOutput.h>
// Uses standard EFI_GRAPHICS_OUTPUT_PROTOCOL_GUID

2.3 Enhanced Debugging Techniques

Serial port debugging was implemented for precise issue localization:

1. Debug information injection at critical points:

   DEBUG((DEBUG_INFO, "Entering GameLoop\n"));

2. Variable state output via SerialPortLib:

   UINTN BufferSize = 1024;
   CHAR8 DebugBuffer[1024];
   AsciiSPrint(DebugBuffer, BufferSize, "FrameBuffer @ 0x%08x\n", FrameBufferBase);
   SerialPortWrite((UINT8*)DebugBuffer, AsciiStrLen(DebugBuffer));

Phase 2 Optimization: Performance Tuning and Architecture Improvements

3.1 Rendering Performance Optimization

Serial log analysis revealed redundant calculations in the rendering module:

• Original code recalculated block positions every frame
• Optimized solution implemented incremental updates:
  ```c
  // Before (full recalculation per frame)
  for (int y = 0; y < BOARD_HEIGHT; y++) {
  for (int x = 0; x < BOARD_WIDTH; x++) {

    // Position calculation logic

  }
  }

// After (incremental update)
static INTN LastBlockPos = -1;
if (CurrentBlockPos != LastBlockPos) {
// Calculate changed areas only
LastBlockPos = CurrentBlockPos;
}

## 3.2 Input Handling Improvements
The original keyboard processing had 50ms latency. The optimized solution:
1. Added input buffer:
```c
#define INPUT_BUFFER_SIZE 32
UINT8 InputBuffer[INPUT_BUFFER_SIZE];
UINTN BufferIndex = 0;

1. Implemented double buffering:
   ```c
   // Write to buffer
   InputBuffer[BufferIndex++] = ScanCode;

// Read in main loop
for (UINTN i = 0; i < BufferIndex; i++) {
ProcessKey(InputBuffer[i]);
}
BufferIndex = 0;
```

Test Results Analysis and Industry Implications

4.1 Core Capability Evaluation

Evaluation Dimension	Score	Key Breakthroughs
Code Generation Quality	4.2/5	Modular design meets engineering standards
Issue Localization Precision	4.5/5	Identifies cross-module dependencies
Debugging Support	4.0/5	Generates targeted debugging suggestions
Performance Optimization	3.8/5	Requires manual validation of solutions

4.2 Developer Practice Recommendations

1. Code Review Process:

   • Conduct static analysis before dynamic debugging
   • Focus on memory management and protocol interactions

2. Debug Information Configuration:

   • Separate outputs by log level
   • Format critical variables in hexadecimal

3. Exception Handling Design:

   • Predefine error code mappings
   • Implement automated recovery mechanisms

4.3 Technology Evolution Directions

Current models require improvement in:

• Multithreading support: Enhance lock mechanisms and concurrency control
• Hardware abstraction layer development: Improve register operation accuracy
• Performance prediction: Add resource consumption estimation capabilities

The testing demonstrates that next-gen LLMs can handle complex system development but require robust code validation mechanisms before production deployment. Developers should position AI tools as assistants, achieving efficiency and quality improvements through human-AI collaboration.