LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems

• URL: http://arxiv.org/abs/2401.05443v1
• Date: Mon, 8 Jan 2024 23:52:42 GMT
• Title: LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems
• Authors: Mohamad Fakih, Rahul Dharmaji, Yasamin Moghaddas, Gustavo Quiros Araya, Oluwatosin Ogundare, and Mohammad Abdullah Al Faruque
• Abstract summary: Large Language Models (LLMs) fail to produce valid programs for Industrial Control Systems (ICS) operated by Programmable Logic Controllers (PLCs) We propose a user-guided iterative pipeline leveraging user feedback and external verification tools including grammar checkers, compilers and SMV verifiers. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model.
• Score: 9.946058168276744
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings. The pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainability, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for Industrial Control Systems (ICS) operated by Programmable Logic Controllers (PLCs). We propose LLM4PLC, a user-guided iterative pipeline leveraging user feedback and external verification tools including grammar checkers, compilers and SMV verifiers to guide the LLM's generation. We further enhance the generation potential of LLM by employing Prompt Engineering and model fine-tuning through the creation and usage of LoRAs. We validate this system using a FischerTechnik Manufacturing TestBed (MFTB), illustrating how LLMs can evolve from generating structurally flawed code to producing verifiably correct programs for industrial applications. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. The proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open research, we share the complete experimental setup, the LLM Fine-Tuning Weights, and the video demonstrations of the different programs on our dedicated webpage.

Related papers

• Precision or Peril: Evaluating Code Quality from Quantized Large Language Models [0.5249805590164902]
  Quantization has emerged as a way to mitigate the memory overhead of Large Language Models. This study aims to evaluate the current code generation capabilities of smaller LLMs using various metrics.
  arXiv  Detail & Related papers  (2024-11-16T01:31:29Z)
• CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement [32.46078765471136]
  We introduce CodeLutra, a novel framework that enhances low-performing large language models. Unlike conventional fine-tuning, CodeLutra employs an iterative preference learning mechanism to compare correct and incorrect solutions. On a challenging data analysis task, using just 500 samples improved Llama-3-8B's accuracy from 28.2% to 48.6%, approaching GPT-4's performance.
  arXiv  Detail & Related papers  (2024-11-07T21:51:07Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents [27.097029139195943]
  Agents4PLC is a novel framework that automates PLC code generation and code-level verification. We first establish a benchmark for verifiable PLC code generation area. We then transition from natural language requirements to human-written-verified formal specifications and reference PLC code.
  arXiv  Detail & Related papers  (2024-10-18T06:51:13Z)
• Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis [0.7580487359358722]
  Large Language Models (LLMs) struggle with accuracy and are unsuitable for high-risk applications. We introduce a solution that divides the code generation into two parts; one to be handled by an LLM and one to be handled by formal methods-based program synthesis.
  arXiv  Detail & Related papers  (2024-09-18T15:59:06Z)
• How Well Do Large Language Models Serve as End-to-End Secure Code Producers? [42.119319820752324]
  We studied GPT-3.5 and GPT-4's capability to identify and repair vulnerabilities in the code generated by four popular LLMs. By manually or automatically reviewing 4,900 pieces of code, our study reveals that large language models lack awareness of scenario-relevant security risks. To address the limitation of a single round of repair, we developed a lightweight tool that prompts LLMs to construct safer source code.
  arXiv  Detail & Related papers  (2024-08-20T02:42:29Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

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.