Daniela Damian

Chenyu Wang, Zhou Yang, Yunbo Lyu et al.

Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness has been the main focus, but industry AI models must also satisfy many other important quality attributes. To understand how these attributes are perceived, the challenges they create, and the solutions used in practice, we identify nine key quality attributes and interview 15 AI practitioners from diverse backgrounds. The interviews show that practitioners prioritize attributes differently depending on context. For example, efficiency can matter more than correctness in real-time applications, while scalability and deployability are no longer seen as primary concerns. Data imbalance emerges as a major obstacle to maintaining model correctness and robustness, and practitioners commonly use mitigation strategies such as active learning. We validate our main findings with a survey of 50 practitioners, which shows that most of the findings are widely recognized. These results can help researchers focus on the attributes practitioners value most and avoid improving one attribute at the expense of others that are considered more critical.

Edith Zavala, Xavier Franch, Jordi Marco et al.

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.