The Internet of Things (IoT) revolution is reshaping how physical devices embedded with software connect to the Internet, facilitating seamless data exchange and driving automation. Industrial IoT (IIoT) extends these capabilities to industrial devices and Cyber-Physical Systems (CPS), driving Intelligent Manufacturing. This integration supports advanced applications like remote monitoring, predictive maintenance, machine learning (ML), and artificial intelligence (AI) optimization, enhancing production efficiency, adaptability, and decisionmaking. However, managing the vast amounts of data generated requires scalable, automated software architectures. Many small and medium-sized enterprises (SME) face challenges in building such systems due to limited resources and expertise, often starting with manual data collection and basic automation.This paper presents a solution: a fully automated, configurable, and scalable software architecture for Intelligent Manufacturing. Our system has been operational for almost one and a half years on 21 plants, processing about 17K tasks, amounting to more than three months of computations. The experimental results show that automation and elasticity have been needed by such systems since the beginning, while scalability is not required during an initial experimentation phase.

Sound static analysis allows one to overapproximate all possible program executions to infer various properties. However, it requires quite some effort to formalize and prove the soundness of program semantics. Most software applications developed nowadays are distributed systems in which different [micro]services communicate through synchronous and asynchronous mechanisms. These applications are composed of programs developed in many programming languages and rely on many technologies. However, sound static analysis might be particularly promising in distributed architectures, where exhaustively (or even partially) testing such systems is often prohibitive. This paper presents our ongoing work on applying LiSA (Library for Static Analysis) to microservices. So far, our effort has focused on one programming language (Python), a few libraries (ROS2, pika, FastAPI, Django), and the architectural reconstruction of distributed applications. However, it already shows some promising results and general patterns that might be followed to develop such analyses.