Safeguarding AI Generated Code: CodeScene Rollout Steps that Work
Benefit from AI-acceleration without the risks by transforming AI tools like Github Copilot and Claude from a simple code generator into an engineering partner that understands and protects code quality with this 4-step guide.
To keep AI generated code in check, using Code Health, a validated code quality model is key: without a connection to business benefits (faster, better), a “code quality” measure would just be a vanity metric, waiting to be sacrificed on the altar of deadlines.
This blog post highlights the importance of strengthening the Inner Developer Loop and turning AI into a Reliable Engineering Partner.
From Tech Debt to Triumph: How Refactoring Speeds Development by 43% provides a statistical model that translates Code Health scores into tangible business value - faster development and fewer defects.
1. Create Your CodeScene Project
Create your first CodeScene project
Run an initial analysis
Configure teams and mark ex-developers (instructions here)
Re-run the analysis for updated insights (instructions here)
Record the current Hotspot Code Health and set a goal to improve this by 1.0 in the next 3 months. E.g. Move from 5.6 to 6.6.
Success Insight: Share this blog post internally to communicate the benefits
2. Pull Risk Forward: Which code is “AI ready” and which isn’t?
Open your CodeScene project
Under Average Code Health click Explore codebase to view. Green files represent your risk-free, AI-friendly code
Success Insight: In order to quickly uplift code that isn’t AI ready, consider CodeScene’s MCP Server combined with CodeScene ACE to perform the necessary refactorings within a matter of minutes.
🎬 See Agentic AI refactoring in action with ACE here (2-mins)
3. Guardrail your (AI) Code Quality & Test Coverage
Guardrail Code Quality (In the terminal): Share this 3-Step Developer Onboarding guide with the team for installing the IDE extension.
Guardrail Test Coverage: Strong automated tests offer much needed protection and no, the tests shouldn't be AI generated from the code (see Cloud setup instructions or On-Premise setup instructions)
Guardrail Code Quality (In PRs): Enable the Pull Request integration with the bare minimum profile as the final gate and safety-net for your team. (see setup instructions)
4. Turn AI Into a Reliable Engineering Partner
You are now primed to strengthen the Inner Developer Loop and Turn AI Into a Reliable Engineering Partner. Leveraging CodeScene’s MCP Server transforms AI from a simple code generator into an engineering partner that understands and protects code quality.
AI Quality Gate: Install CodeScene’s MCP server to safeguard code quality and ensure AI generated tests are of the highest quality.
Developer Enablement: Share this blog post to communicate the benefits and this MCP Server Demo.
Management Enablement: Share this Scale AI coding safely without sacrificing quality overview.
Track the Hotspot Code Health against your 3-month goal in the weekly PDF report. On track? Celebrate the wins. Off track? Revisit adoption steps are carried out.
Success Insight: Teams that follow this structured approach succeed with AI-assisted coding, keeping AI generated code in check and implementing a strong test suite to mitigate the pitfalls. Want to make time consuming refactorings a thing of the past? Teams that excel leverage CodeScene’s MCP Server with CodeScene ACE to quickly uplift code health within a matter of minutes.