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Université de Montréal

Organization2 reports

2 reports, presentations and talks by Université de Montréal in the Game Industry Library, the newest “Democratizing Game Modding with GenAI: A Case Study of StarCharM” (2025).

Documents

Cover of Democratizing Game Modding with GenAI: A Case Study of StarCharM
Whitepaper35 pages

Democratizing Game Modding with GenAI: A Case Study of StarCharM

StarCharM serves as a transformative framework for democratizing game modding by leveraging generative artificial intelligence to lower the technical barriers associated with content creation.

  • StarCharM uses GPT-4o and multi-stage prompt chaining to automate the creation of complex JSON configuration files, including NPC schedules, dialogue, and trait-based gift preferences for Stardew Valley.
  • The framework successfully lowers technical barriers for non-technical users, enabling them to create narrative-driven content through natural language inputs and iterative, scaffolded workflows.
  • 2025 evaluations reveal a tension between accessibility and community standards, specifically citing concerns over content homogenization and the production of repetitive, robotic dialogue.
Polytechnique MontréalOct 2025
Cover of SyDRA: An Approach to Understand Game Engine Architecture
Whitepaper28 pages

SyDRA: An Approach to Understand Game Engine Architecture

The Subsystem-Dependency Recovery Approach, or SyDRA, provides a structured methodology for navigating the inherent complexity of game engine architectures.

  • The Subsystem-Dependency Recovery Approach (SyDRA) improves developer efficiency and accuracy in architectural tasks by visualizing complex dependency models derived from file-include relationships.
  • Empirical analysis of ten open-source game engines identified that core components, specifically low-level rendering and resource management, frequently exhibit high coupling and poor folder-level cohesion.
  • SyDRA functions as a diagnostic tool that enables teams to predict the downstream impacts of code modifications and identify specific refactoring opportunities more effectively than manual inspection.
Concordia UniversityJul 2024

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