AI Bug Prevention System

I’m building BugShield AI, a solution that acts before bugs ever reach production. The goal is clear: use machine-learning or rule-based techniques—whichever you judge best—to stop security vulnerabilities, performance slow-downs, and functional errors at the source for both web and mobile applications.

Here’s the workflow I have in mind: feed code (or pull requests) into the engine, have it analyse patterns, flag risky changes in real time, and suggest concrete fixes or architectural tweaks. Integration with common CI/CD pipelines such as GitHub Actions, GitLab CI or Bitbucket Pipelines is essential so that each commit is automatically scanned prior to merge.

Key deliverables
• A working core engine (Python, Node or comparable) that consumes source files, runs the prevention logic, and outputs actionable recommendations.
• API or CLI endpoints so I can wire the engine into existing build steps.
• Web/mobile friendly reporting dashboard or JSON output for easy parsing.
• Set-up documentation and a short demo project showing the system catching at least one security, performance and functional issue.

Acceptance criteria
1. For a supplied sample repo, the engine must detect and prevent a SQL-injection risk, an N+1 query performance trap, and a failing edge-case condition before tests run.
2. Runtime on a medium-sized codebase should remain under five minutes within CI.
3. All instructions are reproducible on macOS and Linux.

If you have prior experience with static analysis tools, AI code assistants, or automated testing frameworks, let me know—along with a brief outline of your proposed approach and timeline.