SIMCODE: A Benchmark for Natural Language to ns-3 Network Simulation Code Generation
Bibliographic record
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in code generation across various domains. However, their effectiveness in generating simulation scripts for domain-specific environments like ns-3 remains underexplored. Despite the growing interest in automating network simulations, existing tools primarily focus on interactive automation over rigorous evaluation. To facilitate systematic evaluation, we introduce SIMCODE, the first benchmark to evaluate LLMs’ ability to generate ns-3 simulation code from natural language. SIMCODE includes 400 tasks across introductory, intermediate, and advanced levels, with solutions and test cases. Using SIM-CODE, we evaluate three prominent LLMs, Gemini-2.0, GPT-4.1, and Qwen-3, across six prompt techniques. Furthermore, investigating task-specific fine-tuning’s impact reveals that while GPT-4.1 outperforms others, execution accuracy remains modest, with substantial room for improvement. Error analysis identifies missing headers and API mismatches as dominant failures. Nevertheless, SIMCODE provides a foundational step toward evaluating LLMs and research in domain-aware generative systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".