QualiTact: A semantic model for quality-driven development of AI-based systems based on architectural tactics

Systems operating in safety-critical industries increasingly adopt Artificial Intelligence (AI). Satisfaction of system quality attributes in these industries is mandatory and rigorously verified. To influence system quality, a crucial development practice is the use of architectural tactics. However, selecting and balancing these attributes is often based on experience rather than on conscious decisions. This can unexpectedly impact system quality and might be detected too late in the testing phase. For development projects, it is desirable to identify and plan the selection of architectural tactics in a quality-driven manner. This article presents an approach that calculates a tactic’s relative influence between 24 linked quality attributes and 150 tactics as part of a semantic model that supports the quality-driven development of AI-based systems. Attribute selection and filtering create recommendations of relevant tactics for a specific design and development task. In an exploratory multi-case study, the suitability of the recommended tactics was analyzed using source code. Results show that the model can compensate missing quality attribute information input and can provide recommendations. Despite an average input of only 39.98% from 30 case-relevant quality attributes for the model, the average recommendation completeness reaches 49.26% for 20.67 known tactics in source code across cases, leading the model to suffer from gaps between the literature and cases. Yet, 28 new design tactics linked to 30 known quality attributes were identified. Our work thus lays the foundation to support quality-driven design and development by recommending architectural tactics based on quality requirements.