DebtScanner integrates with existing development environments to continuously monitor changes in the codebase of live games.

Using machine learning and pattern recognition, it identifies potential areas of technical debt by analyzing historical code commits, bug reports, and performance metrics.

It provides developers with a visual dashboard that categorizes detected issues by severity, projected impact, and remediation difficulty.

The system also offers automated suggestions for refactoring and communicates the potential benefits of addressing specific issues to project stakeholders.

DebtScanner provides game studios with an automated, proactive approach to managing technical debt, reducing emergency maintenance time, and enabling more predictable development cycles.

This automated debt detection reduces the need for manual code reviews, decreases the likelihood of costly post-release failures, and allows more efficient resource allocation.

The core technology of DebtScanner relies on advances in machine learning, which are mature for analyzing code patterns and anomalies.

Implementing the solution requires integration with version control systems and continuous integration pipelines, achievable with current development workflows.

However, it requires substantial initial training on historical code data specific to each studio's environment to tailor the machine learning models effectively.