Requirements-driven Slicing of Simulink Models Using LLMs

• URL: http://arxiv.org/abs/2405.01695v1
• Date: Thu, 2 May 2024 19:41:04 GMT
• Title: Requirements-driven Slicing of Simulink Models Using LLMs
• Authors: Dipeeka Luitel, Shiva Nejati, Mehrdad Sabetzadeh,
• Abstract summary: We present a method based on large language models (LLMs) for extracting model slices from graphical Simulink models. We explore how different levels of granularity (verbosity) in transforming Simulink models into textual representations, as well as the strategy used to prompt the LLM, impact the accuracy of the generated slices.
• Score: 4.670347587555517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model slicing is a useful technique for identifying a subset of a larger model that is relevant to fulfilling a given requirement. Notable applications of slicing include reducing inspection effort when checking design adequacy to meet requirements of interest and when conducting change impact analysis. In this paper, we present a method based on large language models (LLMs) for extracting model slices from graphical Simulink models. Our approach converts a Simulink model into a textual representation, uses an LLM to identify the necessary Simulink blocks for satisfying a specific requirement, and constructs a sound model slice that incorporates the blocks identified by the LLM. We explore how different levels of granularity (verbosity) in transforming Simulink models into textual representations, as well as the strategy used to prompt the LLM, impact the accuracy of the generated slices. Our preliminary findings suggest that prompts created by textual representations that retain the syntax and semantics of Simulink blocks while omitting visual rendering information of Simulink models yield the most accurate slices. Furthermore, the chain-of-thought and zero-shot prompting strategies result in the largest number of accurate model slices produced by our approach.

Related papers

• Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling [23.447466392929712]
  Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks. Existing LLM ensembling methods often overlook model compatibility and struggle with inefficient alignment of probabilities. We introduce the textscUnion textscTop-$k$ textscEnsembling (textscUniTE), a novel approach that efficiently combines models by focusing on the union of the top-k tokens from each model.
  arXiv  Detail & Related papers  (2024-10-03T08:42:38Z)
• Model Generation with LLMs: From Requirements to UML Sequence Diagrams [9.114284818139069]
  This paper investigates the capability of ChatGPT to generate a specific type of model, i.e., sequence diagrams, from NL requirements. We examine the sequence diagrams generated by ChatGPT for 28 requirements documents of various types and from different domains. Our results indicate that, although the models generally conform to the standard and exhibit a reasonable level of understandability, their completeness and correctness with respect to the specified requirements often present challenges.
  arXiv  Detail & Related papers  (2024-04-09T15:07:25Z)
• Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
  We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary. We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
  arXiv  Detail & Related papers  (2024-03-12T14:58:52Z)
• Representation Surgery for Multi-Task Model Merging [57.63643005215592]
  Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges multiple independently trained models to perform MTL instead of collecting their raw data for joint training. By visualizing the representation distribution of existing model merging schemes, we find that the merged model often suffers from the dilemma of representation bias.
  arXiv  Detail & Related papers  (2024-02-05T03:39:39Z)
• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction [49.510163437116645]
  Click-through rate (CTR) prediction plays as a core function module in personalized online services. Traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality. Pretrained Language Models(PLMs) has given rise to another paradigm, which takes as inputs the sentences of textual modality. We propose to conduct Fine-grained feature-level ALignment between ID-based Models and Pretrained Language Models(FLIP) for CTR prediction.
  arXiv  Detail & Related papers  (2023-10-30T11:25:03Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations [0.0]
  This paper introduces CARE, a modular explanation framework that addresses the model- and user-level desiderata. As a model-agnostic approach, CARE generates multiple, diverse explanations for any black-box model.
  arXiv  Detail & Related papers  (2021-08-18T15:26:59Z)
• Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept [56.46135010588918]
  We prove that the widely used class of RNN-Transducer models and segmental models (direct HMM) are equivalent. It is shown that blank probabilities translate into segment length probabilities and vice versa.
  arXiv  Detail & Related papers  (2021-04-13T11:20:48Z)
• Multi-Fact Correction in Abstractive Text Summarization [98.27031108197944]
  Span-Fact is a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection. Our models employ single or multi-masking strategies to either iteratively or auto-regressively replace entities in order to ensure semantic consistency w.r.t. the source text. Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.
  arXiv  Detail & Related papers  (2020-10-06T02:51:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.