Who this is for

This guide is for teams running a CodeScene trial or proof of value. It helps you decide in two to four weeks whether CodeScene solves your problem, using your own code and your own data.

Week 1

Define what success looks like

Pick the one or two problems you most want to solve and write a measurable success criterion for each. Common goals include:

• Scale AI-assisted coding safely: "AI-generated changes don't lower Code Health in our core systems."

• Reduce technical debt where it hurts: "We can identify and prioritise our top 10 hotspots by business impact."

• Speed up delivery and reduce defects: "We can see where unplanned work and defects come from."

• Reduce knowledge risk: "We can see key-person dependencies and onboarding risk per team."

Agree who will judge the evaluation (for example the CTO, engineering managers or a principal engineer) and when they will do it.

Set up a pilot project

• Choose two to five repositories that matter. Include at least one high-change core system and one AI-heavy or new codebase. Don't pick only small or clean repositories.

• Configure teams and mark former developers.

• Optional but recommended: Connect Jira or Azure DevOps to get delivery and defect insights.

• Cloud trial accounts have project limitations (see What Are the Project Limitations for CodeScene Cloud Trial Accounts?).

Validate the insights against what you know

Ask your most experienced engineers whether CodeScene matches their experience:

• Hotspots: Are the files CodeScene prioritises the ones that cause the most pain?

• Code Health: Do the low-scoring files match the code people avoid touching?

• Knowledge and team insights: Do the key-person risks and coordination bottlenecks look right?

• AI readiness: Which parts of the codebase are healthy enough for AI (under Average Code Health, select Explore codebase), and which aren't? See how here.

Tip: CodeScene's strength is that it combines code quality with how the code is actually developed: Change frequency, teams and knowledge. Evaluate that combination, not just a list of code smells.

Week 2

Test the guardrails in the daily workflow

Have 3–10 developers use CodeScene in their real work (see the 3-Step Developer Onboarding Guide):

• CodeHealth MCP Server in their AI assistant: Does the AI produce healthier code and fix issues when guided?

• CodeScene CLI as a local pre-commit or agent hook: Does it catch regressions before they leave the machine?

• Pull Request integration with the bare minimum profile: Are the review comments relevant, actionable and low-noise?

• IDE extension: Is real-time feedback acted on during manual coding?

Collect feedback on signal-to-noise, speed, and whether developers would keep using it.

Week 3

Measure and decide

Compare the results with your Step 1 criteria. Use this scorecard and rate each line from 1 to 5:

Finish with a short recommendation that includes a baseline and a three-month goal, for example "Hotspot Code Health 5.8 → 6.5, AI adoption 15% → 40%". If you go ahead, these become your success KPIs.

Evaluation checklist

• Success criteria agreed and an owner named

• Representative repositories analysed; teams and former developers configured

• Hotspots and Code Health validated by senior engineers

• AI readiness mapped

• MCP Server, CLI and PR integration tested by real developers

• Scorecard completed with a baseline and goal defined

Need help? Your CodeScene contact can run a guided evaluation kickoff if needed.

Related articles

• Getting Started Guide For New Users

• 3-Step Developer Onboarding Guide

• Safeguarding AI Generated Code: CodeScene Rollout Steps that Work

• Reducing Technical Debt: CodeScene Rollout Steps that Work