AI Playtesting System for Automated Visual Bug Detection
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Solution Overview
Problem
The increasing complexity of computer games has made traditional playtesting inefficient, as human testers need to spend years exploring numerous possible game states to identify and fix bugs, while current automated systems are limited in detecting visual errors and may miss critical areas, requiring significant human intervention.
Innovation Solution
An intelligent playtesting system using machine learning and AI to simulate player interactions, analyze visual data, and determine optimal testing routes, enabling comprehensive, automated testing by training bots to identify bugs and glitches through image processing and object recognition, and dynamically adjusting testing paths and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive automated testing systems are implemented, then testing productivity and coverage are improved, but the system complexity and difficulty of detecting visual errors increase
Solution Approach 1:
The patent introduces an AI-based visual analysis system as an intermediary between automated gameplay bots and bug detection. This intermediary processes visual data from game screens, enabling comprehensive automated testing of visual errors without requiring direct human intervention. The AI system acts as a mediator that bridges the gap between automated testing efficiency and human-level visual detection capability.
Solution Approach 2:
The patent replaces manual visual inspection by human testers with automated AI-based image processing and analysis. Machine learning models analyze game screenshots and video feeds to detect visual bugs, replacing the mechanical process of human visual examination. This substitution enables continuous automated testing while maintaining high detection accuracy for visual errors.
2Loss of time
If automated testing systems are used, then testing time is reduced, but detection precision for visual errors and complex bugs deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where AI analysis results are continuously refined based on detected patterns and confirmed bugs. The system learns from each detection event, improving its precision over time. Human reviewers provide feedback on AI-detected issues, which is used to retrain and improve the AI models, creating a continuous improvement loop that maintains high detection precision while preserving automated testing speed.
Solution Approach 2:
The patent divides the testing system into specialized AI modules, each trained to detect specific types of visual errors (e.g., texture errors, model clipping, UI bugs). This segmentation allows each module to achieve high precision for its specific detection task while working together in parallel to provide comprehensive coverage. The modular approach enables the system to maintain high overall detection precision across multiple bug types.
3Measurement precision
If AI-based visual analysis is implemented, then detection capability for visual errors is improved, but computational energy consumption and processing time increase
Solution Approach 1:
The patent applies partial action by having the AI visual analysis system focus only on specific regions of interest in game screens rather than analyzing entire high-resolution displays. The system identifies and analyzes only areas where visual errors are likely to occur (e.g., character models, UI elements, collision points), reducing computational energy consumption while maintaining detection capability for critical visual bugs.
Solution Approach 2:
The patent implements preliminary action by pre-processing game visual data to identify regions of interest before detailed AI analysis. The system performs initial scanning to detect areas with potential visual anomalies, then applies computationally intensive AI analysis only to those specific regions. This preliminary filtering reduces overall computational energy consumption while preserving detection capability for visual errors.
Data Source
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AI summary
A data processing system comprises an information input unit operable to receive information relating to computer gameplay of the computer game, an error characterising unit operable to identify indicators of an error in the computer game based upon the received information, a playtest control unit operable to control one or more playtest bots in the computer game to generate playtest data, the playtest data comprising information relating to the identified indicators of an error, an error reporting unit operable to identify errors in dependence upon the playtest data.