Context: The Explainability term is increasingly used in informatics literature, especially in relation to Artificial Intelligence, but it is unclear whether and how it is used in the Software Architecture (SA) community. Objective: This article describes the design and results of a study of the presence and evolution of explainability-related concepts in SA discourse, in both academic and practitioner literature. Method: Topic analysis was done using Latent Dirichlet Allocation (LDA) on postings/articles tagged as "software architecture," between 2008 and 2024, on several representative practitioner sources (StackOverflow, InfoQ and DZone) and a researcher source (IEEE Xplore). Results: A search for "explainability," "observability" and other Explainability-related terms (as identified in previous work) yielded over 48,000 online primary documents. Besides descriptive results on relative use of terms, a key finding is that academic literature focuses on Traceability, but practitioner discussions focus on Observability. Conclusions: The terminology gap between academia and industry on Explainability-related terms matches other reported academia-industry vocabulary differences. This line of research may help to identify alternative concepts in use for a term and alternative terms in use for a concept, and align practitioner-research exchanges on Explainability-related concepts.

With growing system complexity, explainability is being heard more frequently in software development ecosystems. However, it is unclear whether this situation also exists among software architects in industry. This paper reports the design, execution, and findings of a rapid multivocal review exploring perspectives on explainability within current software architecture discourse. Analysis of seven well-known practitioner and academic sources finds that “explainability” is used in discussions about documenting trade-offs, surfacing assumptions, and framing architecture maps, all of which resonate with motivations for comprehensible, adaptable design. These insights may allow the research community to build further bridges with practitioners for sustaining collective system understanding.