Explainability in Software Architectural Decisions: The ADR-E Framework and Empirical Evaluation

As software systems increase in scale and complexity, architectural decisions must be transparent, traceable, and understandable to diverse stakeholders. However, traditional documentation approaches—such as standard Architectural Decision Records (ADRs)—often lack the structured rationale and contextual detail necessary to support informed analysis and long-term architectural stewardship. This paper presents the Software Architecture Explainability Framework (SAEF), a structured approach for enabling explainable architectural decision-making. Central to the framework is the Explainable Architectural Decision Record (ADR-E), which extends traditional ADRs with explicit rationale, structured stakeholder-oriented explanations, rejected alternatives, and traceability links grounded in explainability principles inspired by AI. SAEF was evaluated through two industrial case studies: the selection of Azure Kubernetes Service for container orchestration and the adoption of an enterprise-grade observability platform. Using a mixed-methods design combining workshops, scenario-based simulations, surveys, interviews, and operational metrics, the study found that ADR-E substantially improved transparency, traceability, and stakeholder alignment. Both cases reported a 30% reduction in mean time to resolution (MTTR) and transparency scores above 4.6/5. Overall, SAEF provides a practical and theoretically grounded foundation for explainable architectural decision-making. Future work will focus on tool support, graphical notations, and longitudinal assessments to enhance adoption and scalability.