PinGA: Semi-Automated Provenance Instrumentation for Digital Games
Resumo
Introduction: Game telemetry techniques commonly record events and state changes but fail to explicitly represent causal relationships between gameplay actions. Provenance-based approaches address this limitation by modeling cause-and-effect through provenance graphs; however, existing solutions such as the Provenance in Games (PinG) framework and its Unity implementation (PinGU) rely on extensive manual instrumentation, which limits scalability and adoption. Objective: This work proposes PinGA, an automation-oriented approach for provenance instrumentation in digital games. PinGA aims to reduce the manual effort required to configure provenance tracking while preserving the causal expressiveness of provenance-based gameplay analysis. Methodology: PinGA defines a semi-automated workflow based on three main steps: the discovery of monitorable gameplay elements, developer-guided semantic configuration of those elements in provenance terms, and runtime monitoring for provenance graph generation. To evaluate the feasibility of this proposal, PinGA was instantiated in the Godot game engine through PinGODOT and its editor-integrated automation layer, PinGODOT Manager. Results: A preliminary evaluation conducted on a 2D game prototype showed that the workflow operated end-to-end, shifted a substantial part of manual instrumentation to editor-based configuration, and generated provenance graphs that supported causal inspection of the evaluated gameplay session.
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