LLM-based Control Code Generation using Image Recognition

LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes. Previous attempts in control logic code generation lacked methods to interpret schematic drawings from process engineers. Recent LLMs now combine image recognition, trained domain knowledge, and coding skills. We propose a novel LLM-based code generation method that generates IEC 61131-3 Structure Text control logic source code from Piping-and-Instrumentation Diagrams (P&IDs) using image recognition. We have evaluated the method in three case study with industrial P&IDs and provide first evidence on the feasibility of such a code generation besides experiences on image recognition glitches.Comment: 8 pages, 8 figure

Towards Reverse Engineering for Component-Based Systemswith Domain Knowledge of the Technologies Used

Many developers today face the challenge of managing and maintaining existing legacy software systems. Improving the understanding of these systems is an important issue in addressing these challenges. To improve understanding, reverse engineering can be used to generate a higher-level representation. However, generic and extensible reverse engineering solutions that address multiple types of different technologies are missing or incomplete. This paper proposes to take a step in this direction. We describe the underlying idea of how used technologies such as frameworks and libraries induce parts of the architecture. Building on this, we describe our proposed approach of how the similarities of different technologies can be used to redevelop component-based architectures. By incorporating knowledge about technologies, we aim to improve the result of reverse engineering processe

Performance Modelling of Message-Oriented Middleware with Priority Queues

Message-Oriented Middleware (MOM) with priority queues reduces the latency of critical events. In general, MOM uses a FIFO queuing methodology. But, different application scenarios require certain critical events with higher priority to be served earlier over low-priority events, so that the subscriber of the event consumes the high-priority event with less delay. In the context of the Palladio Component Model (PCM), MOM-based systems have been modelled considering message queue length and latency as metrics for performance prediction and simulation. However, the approaches did not consider modelling MOM with priority queues and their impact on performance. We will first, discuss the existing approaches in PCM which support performance prediction for MOM-based systems and then propose how they can be extended to support performance predictions for MOM with priority queuing. We will then conclude which approach is best suited to extend by assessing their capabilities to predict performance metrics relevant for priority queuing, especially the delay of individual events at the subscriber end

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Quality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques

Easing the Reuse of ML Solutions by Interactive Clustering-based Autotuning in Scientific Applications

Software is disrupting one industry after another. Currently, the automotive industry is under pressure to innovate in the area of software. New, innovative approaches to vehicles and their HW/SW architectures are required and are currently subsumed under the term “SW-defined vehicle”. However, this trend does not stop at the vehicle boundaries, but also includes communication with off-board edge and cloud services. Thinking it through further, this leads to a breakthrough technology we call “Reliable Distributed Systems”, which enables the operation of vehicles where time and safety-critical sensing and computing tasks are no longer tied to the vehicle, but can be shifted into an edge-cloud continuum. This allows a variety of novel applications and functional improvements but also has a tremendous impact on automotive HW/SW architectures and the value chain. Reliable distributed systems are not limited to automotive use cases. The ubiquitous and reliable availability of distributed computing and sensing in real-time enable novel applications and system architectures in a variety of domains: from industrial automation over building automation to consumer robotics. However, designing reliable distributed systems raises several issues and poses new challenges for edge and cloud computing stacks as well as electronic design automation

Architecture-driven requirements prioritization

Quality requirements are main drivers for architectural decisions of software systems. However, in practice they are often dismissed during development, because of initially unknown dependencies and consequences that complicate implementation. To decide for meaningful, feasible quality requirements and trade them off with functional requirements, tighter integration of software architecture evaluation and requirements prioritization is necessary. In this position paper, we propose a tool-supported method for architecture-driven feedback into requirements prioritization. Our method uses automated design space exploration based on quantitative quality evaluation of software architecture models. It helps requirements analysts and software architects to study the quality trade-offs of a software architecture, and use this information for requirements prioritization