ArcAssist-Web: Towards a Logical Reference Architecture for GenAI-Assisted Web Architectural Evolution

  • Vanusa Rodrigues de Oliveira UFLA
  • Bento Rafael Siqueira UFLA
  • Samira Santos da Silva UFLA
  • Johnatan Alves Oliveira UFLA

Resumo


Web and PWA evolution often requires architectural decisions that cross user-interface, API, data, privacy, and administrative-review boundaries. Generative AI can assist architects in exploring alternatives, provided that its outputs remain grounded in the current Web-system baseline, explicit about assumptions, and subject to human acceptance. This paper presents ArcAssist-Web, a logical reference-architecture proposal for evidence-grounded GenAI assistance in Web architectural evolution. It organizes seven reusable responsibilities: interaction, Web-system and domain context, context and knowledge, prompt orchestration, LLM reasoning, validation and QA, and knowledge management. The proposal is illustrated with an animal-rescue Web/PWA adoption-interest increment. A feasibility-oriented traceability evaluation compares generic prompting with an ArcAssist-Web configuration using frozen Web/domain evidence and a Decision Package contract. Across three retained pairs, both conditions could reach a similar architectural direction, while the configured outputs cited all 15 evidence identifiers and made Web context, assumptions, and validation plans explicit. Because the prompts imposed different output structures, these observations show inspectability and instruction conformance rather than a causal condition effect or architectural correctness.

Palavras-chave: Generative AI, Web systems, software architecture, architectural evolution, architectural decisions, reference architecture

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Publicado
27/10/2026
OLIVEIRA, Vanusa Rodrigues de; SIQUEIRA, Bento Rafael; SILVA, Samira Santos da; OLIVEIRA, Johnatan Alves. ArcAssist-Web: Towards a Logical Reference Architecture for GenAI-Assisted Web Architectural Evolution. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 32. , 2026, Lavras/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2026 . p. 541-545. DOI: https://doi.org/10.5753/webmedia.2026.30266.