
Joshua Stiles developed and enhanced the RebelRemind platform in the UNLV-CS472-672/2025-S-GROUP3-RebelRemind repository, focusing on robust event data integration and backend reliability. He architected a modular data model using Python, SQLAlchemy, and Flask, separating event sources into script-specific tables with a unified daily aggregation. Joshua implemented Selenium-based web scraping, standardized JSON outputs, and modernized database integration to improve data consistency and maintainability. He expanded testing infrastructure with persistent test databases and comprehensive API coverage, while also updating React frontend components for multi-source event display. His work addressed cross-platform deployment issues and streamlined development cycles through improved documentation and QA.

April 2025 performance summary for UNLV-CS472-672/2025-S-GROUP3-RebelRemind: Delivered a unified event data model with organization scope, extended event records with startDate/startTime, and integrated multi-source data into the UI (AccordionMenu). Strengthened QA and testing with a persistent test database, broader API/database coverage, and standardized responses, reducing race conditions and improving test reliability. Improved documentation and setup practices. Result: more accurate event data across organizations, faster development cycles, and higher confidence in production deployments.
April 2025 performance summary for UNLV-CS472-672/2025-S-GROUP3-RebelRemind: Delivered a unified event data model with organization scope, extended event records with startDate/startTime, and integrated multi-source data into the UI (AccordionMenu). Strengthened QA and testing with a persistent test database, broader API/database coverage, and standardized responses, reducing race conditions and improving test reliability. Improved documentation and setup practices. Result: more accurate event data across organizations, faster development cycles, and higher confidence in production deployments.
March 2025: Implemented end-to-end improvements to RebelRemind data pipeline, delivering new scraping, data modeling, and output standardization. Key outcomes include a modular data model with separate script-specific tables and a central Daily table, standardized JSON outputs and DB integration, and a strengthened test framework. Also resolved cross-platform issues and enhanced deployment reliability. These changes improve data reliability, cross-team maintainability, and faster iteration across scraping, processing, and API layers.
March 2025: Implemented end-to-end improvements to RebelRemind data pipeline, delivering new scraping, data modeling, and output standardization. Key outcomes include a modular data model with separate script-specific tables and a central Daily table, standardized JSON outputs and DB integration, and a strengthened test framework. Also resolved cross-platform issues and enhanced deployment reliability. These changes improve data reliability, cross-team maintainability, and faster iteration across scraping, processing, and API layers.
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