Supporting Code Generation Based on Design Information

As SDV development advances, automotive software continues to grow in both functional scope and code size, leading to increased implementation effort and review workload.
In addition, differences in how requirements and design are interpreted can become a source of implementation errors and quality inconsistencies.
This feature uses generative AI to analyze requirement documents and design information, supporting code generation that reflects the intended design.
By assisting developers in translating design intent into implementation more consistently, it helps reduce implementation effort while supporting improved development efficiency and stable software quality.

Improving Efficiency of Repetitive Tasks and Stabilizing Quality

By leveraging generative AI, it becomes easier to streamline routine and repetitive implementation tasks.
– Generating code structures aligned with design intent
– Supporting implementation with a focus on consistency between requirements and code
– Reducing the workload associated with recurring implementation tasks
As a result, developers can devote more time to higher‑value activities such as architecture design and quality improvement, helping enhance both development productivity and the stability of software quality in automotive software development.

Supporting Change‑Friendly Development

In SDV development, specification changes and feature additions are expected.
By structuring the flow from requirements, specifications, and design inputs through to code generation, this feature makes follow‑up work during specification changes easier to carry out efficiently.
As a result, it helps reduce gaps between design intent and implementation while supporting flexible and continuous software evolution.