Abstract
Architectural uncertainties arising from incomplete or unclear information pose significant challenges when making architectural decisions in Agile teams. Based on a limited number of case studies that employed a technique called ArchHypo, four patterns were identified that propose small adjustments in the development process to handle architectural uncertainties: PROTECTIVE GUIDELINE, BRING THE SPECIALIST, PLAN FOR PREPARATION, and QUALITY CHECKPOINT. Although the patterns derived from these experiences can be useful in real projects, their applicability and consequences were based on limited evidence and specific scenarios. To address this issue, this paper presents an interview study with experienced software architects and engineers to gather further information on the application of these patterns. The research method employed semi-structured interviews to gather the experiences of professionals with the target practices, and thematic analysis was used to assess their recurrence, applicability, and consequences. The findings confirmed that most professionals recognized those practices in real projects and their suitability as actions in uncertainty management. Moreover, new positive and negative consequences, not previously documented in the patterns, were identified. As a result, this work contributes to the field by providing guidance to professionals on how to better evaluate the trade-offs of those patterns when applied to architecture uncertainty management.

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Virtual Reality allows users to experience realistic environments in an immersive and controlled manner, particularly beneficial for contexts where the real scenario is not easily or safely accessible. The choice between 360 content and 3D models impacts outcomes such as perceived quality and computational cost, but can also affect user attention. This study explores how attention manifests in VR using a 3D model or a 360 image rendered from said model during visuospatial tasks. User tests revealed no significant difference in workload or cybersickness between these types of content, while sense of presence was reportedly higher in the 3D environment.

Abstract
Large language model (LLM)-powered assistants are increasingly used for generating program code and unit tests, but their application in acceptance testing remains underexplored. To help address this gap, this paper explores the use of LLMs for generating executable acceptance tests for web applications through a two-step process: (i) generating acceptance test scenarios in natural language (in Gherkin) from user stories, and (ii) converting these scenarios into executable test scripts (in Cypress), knowing the HTML code of the pages under test. This two-step approach supports acceptance test-driven development, enhances tester control, and improves test quality. The two steps were implemented in the AutoUAT and Test Flow tools, respectively, powered by GPT-4 Turbo, and integrated into a partner company's workflow and evaluated on real-world projects. The users found the acceptance test scenarios generated by AutoUAT helpful 95% of the time, even revealing previously overlooked cases. Regarding Test Flow, 92% of the acceptance test cases generated by Test Flow were considered helpful: 60% were usable as generated, 8% required minor fixes, and 24% needed to be regenerated with additional inputs; the remaining 8% were discarded due to major issues. These results suggest that LLMs can, in fact, help improve the acceptance test process, with appropriate tooling and supervision.