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A legacy uplift that would traditionally have been a 12–18 month, high-risk engineering project was completed in three weeks of part-time work for roughly $4,000 in AI costs.
CodeHealth is an aggregated metric based on 25+ factors scanned from the source code. The code health factors correlate with increased maintenance costs and an increased risk for defects, and predicts whether AI agents will introduce defects or not.
Healthy code works better for both agents and humans 1. Healthy code is more AI-friendly: it is easier for coding agents to analyze, modify, and extend without introducing unintended side effects. High-quality code has never been more important than in the AI era.2.
CodeScene’s new PR Refactoring Agent analyzes the branch using deterministic Code Health data, performs targeted refactorings, and commits improvements back to the PR. The result is code that’s easier to review, AI-friendly, and more token efficient.
Our research across C++, Java, and Python shows that agents working on unhealthy codebases consume 35-45% more tokens to complete the same tasks. That’s in addition to the increased defect risk covered in earlier research. You're not just getting worse output.
Most enterprises are now on a trajectory towards agentic AI. The general assumption is that faster coding will translate into increased productivity across the board.However, there’s a potential clash between expectations and reality: The Code Health benchmarks show that the average Hotspot Code…
This year has marked an interesting and fundamental shift. I’ve been coding for almost 40 years, 30 of those professionally, and yet I’m now at a point where 100% of my production code is written and maintained by AI agents.
What if AI could accelerate development without leaving a trail of complexity, defects, and hard-to-reason-about code that even your own AI agents fail to understand? That’s the promise of a well-designed inner developer loop: a feedback system that keeps AI productive and predictable.
Explore explainable AI for software engineering: from code generation to program repair, discover why explainability is key to trust and adoption.
Technical debt slows teams down. Learn how explicit requirements, ISO 5055 standards, and CodeScene’s Code Health can guide sustainable code quality.
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