Nowadays, the size and complexity of the automotive development life-cycle increase the possibility of cyber-attacks. In this context, team developers play a primary role in managing cyber security, risk assessment, and all phases of software application development (concept phases, product development, cyber security validation, production, operations, and maintenance). Currently, only generic standards exist and they are difficult to put into operation due to the lack of the required skills and knowledge. Therefore, this paper presents a vision model based on Quantum Artificial Intelligence that supports developers' decisions to integrate concrete design methods in the automotive development life-cycle. Organizations need to develop their process for developing vehicle components that comply with the new automotive standards. We suggest the usage of existing data sources (e.g., existing taxonomies) on Quantum Artificial Intelligence algorithms to suggest the best way, or the correct steps, to follow time by time to achieve user solutions.
QAI4ASE: Quantum artificial intelligence for automotive software engineering
De Vincentiis M.;Pagano A.;Piccinno A.
2022-01-01
Abstract
Nowadays, the size and complexity of the automotive development life-cycle increase the possibility of cyber-attacks. In this context, team developers play a primary role in managing cyber security, risk assessment, and all phases of software application development (concept phases, product development, cyber security validation, production, operations, and maintenance). Currently, only generic standards exist and they are difficult to put into operation due to the lack of the required skills and knowledge. Therefore, this paper presents a vision model based on Quantum Artificial Intelligence that supports developers' decisions to integrate concrete design methods in the automotive development life-cycle. Organizations need to develop their process for developing vehicle components that comply with the new automotive standards. We suggest the usage of existing data sources (e.g., existing taxonomies) on Quantum Artificial Intelligence algorithms to suggest the best way, or the correct steps, to follow time by time to achieve user solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
