CodeHealth as the True North for Coding Agents

Important results from the project

The project achieved its goal of establishing collaboration with a US-based company developing language models for source code. We initiated collaboration with NVIDIA on reinforcement learning for their Nemotron model to generate highly maintainable C++ code. This uses CodeHealth as a reward signal. The project also created synergies with the development of CodeScene’s MCP server, and new research showing that code agents are more effective at generating test cases for code with high CodeHealth.

Expected long term effects

In the long term, the project is expected to improve quality assurance of AI-generated code. We have shown that CodeHealth can serve as a compass for code agents via an MCP server. Next, we aim to demonstrate that CodeHealth can also be used as a reward signal when training large language models specialized in maintainable code for specific programming languages. This can reduce technical debt, strengthen CodeScene’s product development, and create new international R&D opportunities.

Approach and implementation

The project was carried out as planned, including preparations, stakeholder mapping, demo materials, a US visit, and follow-up meetings. The activities were well chosen, and the schedule was maintained. Two people from CodeScene attended NVIDIA GTC, strengthening both market intelligence and networking. A positive outcome was that conference discussions quickly led to active project meetings with NVIDIA.

External links

CodeHealth as a Prerequisite and Compass for Coding Agents