AIDOaRt: AI-augmented Automation for DevOps, a model-based framework for continuous development in Cyber–Physical Systems

• DOI: 10.1016/j.micpro.2022.104672
• Publication Date: 2022-09-09T15:24:27Z
• Authors: Hugo Bruneliere; Vittoriano Muttillo; Romina Eramo; Luca Berardinelli; Abel Gómez; Alessandra Bagnato; Andrey Sadovykh; Antonio Cicchetti
• Keywords: sistemes ciberfísics; inteligencia artificial; desarrollo continuo; ingeniería de software; [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]; 02 engineering and technology; Continuous Development; Cyber-Physical Systems; Continuous development; System engineering; Software engineering; Model Driven Engineering; Artificial Intelligence; DevOps; AIOps; System Engineering; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; intel·ligència artificial; Continuous System Engineering; DevOps; AIOPS; Software engineering; Scientific computing; Simulation and Modelling Tools; Cyber-Physical Systems; Artificial Intelligence; Artificial Intelligence; enginyeria basada en models; 0202 electrical engineering, electronic engineering, information engineering; system engineering; Scientific computing; model driven engineering; DevOps; Software engineering; Cyber-Physical Systems; AIOPS; AIOps; Simulation and Modelling Tools; Software Engineering; enginyeria de programari; cyber–physical systems; artificial intelligence; 004; 620; ingeniería basada en modelos; enginyeria de sistemes; Model Driven Engineering; ingeniería de sistemas; continuous development; Continuous System Engineering; desenvolupament continu; software engineering; sistemas ciberfísicos

The advent of complex Cyber–Physical Systems (CPSs) creates the need for more efficient engineering processes. Recently, DevOps promoted the idea of considering a closer continuous integration between system development (including its design) and operational deployment. Despite their use being still currently limited, Artificial Intelligence (AI) techniques are suitable candidates for improving such system engineering activities (cf. AIOps). In this context, AIDOaRT is a large European collaborative project that aims at providing AI-augmented automation capabilities to better support the modeling, coding, testing, monitoring, and continuous development of CPSs. The project proposes to combine Model Driven Engineering principles and techniques with AI-enhanced methods and tools for engineering more trustable CPSs. The resulting framework will (1) enable the dynamic observation and analysis of system data collected at both runtime and design time and (2) provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases. This paper describes the main research objectives and underlying paradigms of the AIDOaRt project. It also introduces the conceptual architecture and proposed approach of the AIDOaRt overall solution. Finally, it reports on the actual project practices and discusses the current results and future plans.