Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 48 references
Medicine
TL;DR
Running fully local without cloud data transmission, this pipeline offers a privacy-safe lightweight solution for SysML PlantUML modeling and does not support SysML-exclusive requirement or parametric diagrams.
Abstract
Model-based systems engineering (MBSE) takes Systems Modeling Language (SysML) as the industrial standard modeling language, yet cloud Large Language Model (LLM)-based SysML generation faces limited domain data, model hallucinations, high hardware cost and confidential data leakage risks. This paper builds a 914-sample SysML PlantUML corpus and proposes a fully offline lightweight framework based on Qwen2.5-Coder-7B-Instruct, integrating 4-bit NF4 Quantized Low-Rank Adaptation (QLoRA) fine-tuning, vector-free Jaccard same-diagram reference retrieval and a three-round syntax correction loop. PlantUML executes syntax parsing while Graphviz only renders layouts. Tested on 131 samples covering five structural and behavioral SysML v1 diagram types, the plain-prompt baseline achieves word-set semantic F1 of 52.94%, and the retrieval-enhanced variant lifts the zero-retry syntax pass rate from 92.37% to 99.28%, with F1 slightly dropping to 50.64%. Running fully local without cloud data transmission, this pipeline offers a privacy-safe lightweight solution for SysML PlantUML modeling and does not support SysML-exclusive requirement or parametric diagrams.
XREPOTEST is introduced, a multilingual repository-level benchmark for unit test generation spanning five underexplored languages: Rust, Go, Julia, PHP, and Ruby, and Invocation Rate is proposed to assess whether generated tests meaningfully exercise the intended functionality.
L. Dung, Dong Cao Van, Nam Le Hai et al.· 0 citations
RTLCurator is presented, which learns a behavior-aware compatibility prior by contrasting each specification with implementations that fail simulation, and calibrates it to a new corpus using a small number of validated pairs, and constructs the retained subset by balancing alignment, representation coverage, and RTL structural richness.
Siyang Cai, Cangyuan Li, Wenjing Chang et al.· arXiv.org· 0 citations
Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correctness on a single syntactic representation of an input. Such point accuracy does not reveal whether a model's correct output is stable when the same RTL behavior is written differently. This paper presents a controlled metamorphic evaluation of LLM-based SVA generation under semantics-preserving RTL transformations. Starting from the VERT dataset, we construct a quality-filtered conditional-control pool and a stratified 40-program evaluation set containing 295 assignment behaviors. We evaluate two open code models, Qwen2.5-Coder-7B and DeepSeek-Coder-V2-Lite, with an identical evaluation prompt and greedy decoding. Three transformations are studied: operand reordering, deterministic identifier renaming, and redundant parenthesization. Beyond baseline and transformed accuracy, we measure conditional robustness, invariance failure, and any-flip rate, with 10,000-sample clustered bootstrap intervals at the RTL-program level. Across all six model-transformation conditions, 9.7%-27.0% of behaviors that were correct on the original RTL become incorrect after a semantics-preserving transformation. Aggregate accuracy can therefore hide substantial instability: under identifier renaming, DeepSeek-Coder-V2-Lite improves from 53.9% to 63.7% accuracy while 19.5% of its originally correct behaviors fail. Manual review of 30 sampled correct-to-wrong transitions identifies dropped path predicates, branch-polarity errors, Boolean-structure corruption, and output-contract violations. The results show that point accuracy alone is insufficient for characterizing LLM reliability in assertion generation and motivate robustness-aware evaluation for AI-assisted hardware verification.
A two-stage approach to automating BSW configuration generation that generalizes to any ECU Configuration (ECUC)-based module: a fine-tuned large language model (LLM) generates a compact JSON intermediate representation capturing only semantically meaningful parameters, which a deterministic expansion function reconstructs into schema-conformant ARXML.
Amr Samy, Ahmed Moro, M. Taher· Applied Sciences· 0 citations
NoTB is introduced, an oracle-free triage framework that infers correctness from cross-model formal consensus and demonstrates that formal cross-model agreement provides a reliable basis for high-confidence triage without model-dependent oracles.
Elisavet Lydia Alvanaki, Je Yang, Biruk B. Seyoum et al.· 0 citations
This study discloses the model efficiency for different tasks, causes of failed fs tasks, and techniques for mitigating LLM failures, and will open source \phi-Bench to facilitate public research on using LLMs for fs development.
Yu Xue, Daixuan Li, Jian Huang· 0 citations
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