EVOSYSML: TRAINING A SPECIALIZED MODEL FOR SYSML V2 CODE GENERATION

Automatically generating SysML v2 code from natural-language requirements can reduce the cost of model-based systems engineering, but progress is hindered by scarce requirement–code data and the limited SysML knowledge of general-purpose language models. We present EvoSysML, a framework for training and deploying a specialized SysML generator, which consists of four stages. (1) An API teacher constructs cold-start data through breadth and depth evolution. (2) Local role models continue both processes, reconstruct aligned requirements, and filter candidates into a validated 50K training corpus. (3) Supervised fine-tuning and syntax-directed direct preference optimization train the final generator. (4) At deployment, formal validation selects an output from multiple sampled candidates. We also introduce EvoSysML-204, a manually verified bilingual benchmark containing 204 aligned requirements per language across three difficulty levels. Against 12 strong baselines, EvoSysML achieves the highest average bilingual syntax pass rate using single-pass inference. Furthermore, compared to strong API models, our method can sample ten reasoning paths under an equivalent time budget, increasing syntax pass rates to 39.22% in English and 37.25% in Chinese. This outperforms the best-performing baselines in each language (Kimi-K2.7-Code and Claude-Sonnet-5) by 18.14 and 19.11 percentage points, respectively. Among syntax-valid outputs, EvoSysML achieves conditional semantic averages of 86.90% and 77.34%, ranking first in English and tying for first in Chinese. Ablation studies confirm that data evolution, two-stage post-training, and validation-guided candidate selection all contribute to the final performance.