Carmen Derderian contributed to the Campus-Compass-SOEN-390/Watermelons_SOEN390 repository by building and refining real-time shuttle data processing and routing features. She implemented robust JavaScript and React Native modules for extracting, formatting, and integrating shuttle information, ensuring accurate travel time estimates and seamless route calculations. Her work included expanding automated testing with Jest and Maestro, stabilizing CI/CD workflows, and improving dependency management. Carmen also focused on backend maintainability by cleaning up the Shuttle Schedule API, removing dead code, and increasing test coverage. These efforts enhanced data reliability, reduced debugging time, and supported faster, more predictable development for the team.
April 2025: Focused on codebase cleanliness and maintainability of the Shuttle Schedule API in Watermelons_SOEN390. Delivered a targeted cleanup that removes dead code (unused test mode and mock schedule functions) to simplify the API surface, reduce maintenance burden, and support faster iteration on related features. Enhanced test coverage for shuttleSchedule.js to ensure reliability and guard against regressions. These changes enable smoother onboarding for new contributors and a clearer module boundary, delivering measurable business value through reduced debugging time and more predictable development velocity.
April 2025: Focused on codebase cleanliness and maintainability of the Shuttle Schedule API in Watermelons_SOEN390. Delivered a targeted cleanup that removes dead code (unused test mode and mock schedule functions) to simplify the API surface, reduce maintenance burden, and support faster iteration on related features. Enhanced test coverage for shuttleSchedule.js to ensure reliability and guard against regressions. These changes enable smoother onboarding for new contributors and a clearer module boundary, delivering measurable business value through reduced debugging time and more predictable development velocity.
March 2025 monthly summary for Campus-Compass-SOEN-390/Watermelons_SOEN390: Focused on delivering a more reliable routing experience, strengthening test infrastructure, and accelerating release readiness through Maestro and CI improvements. Key outcomes include Google Maps travel time estimation with refined shuttle routing, integration of shuttle travel option into route calculations, and UI polish for shuttle popups. The team also expanded automated testing (Jest) with mocks and broader coverage (calendar fetching, shuttle utilities, Google Maps API interactions), and stabilized the CI pipeline by adding dependencies to fix CI issues and introducing Maestro-based test workflows.
March 2025 monthly summary for Campus-Compass-SOEN-390/Watermelons_SOEN390: Focused on delivering a more reliable routing experience, strengthening test infrastructure, and accelerating release readiness through Maestro and CI improvements. Key outcomes include Google Maps travel time estimation with refined shuttle routing, integration of shuttle travel option into route calculations, and UI polish for shuttle popups. The team also expanded automated testing (Jest) with mocks and broader coverage (calendar fetching, shuttle utilities, Google Maps API interactions), and stabilized the CI pipeline by adding dependencies to fix CI issues and introducing Maestro-based test workflows.
February 2025 performance summary focusing on strengthening real-time shuttle data reliability in Campus-Compass-SOEN-390/Watermelons_SOEN390. Implemented extractShuttleInfo for processing and formatting real-time shuttle data and added end-to-end tests to ensure robust handling of valid data, missing data, and empty arrays. This work reduces data parsing errors and establishes a reliable foundation for the broader data pipeline.
February 2025 performance summary focusing on strengthening real-time shuttle data reliability in Campus-Compass-SOEN-390/Watermelons_SOEN390. Implemented extractShuttleInfo for processing and formatting real-time shuttle data and added end-to-end tests to ensure robust handling of valid data, missing data, and empty arrays. This work reduces data parsing errors and establishes a reliable foundation for the broader data pipeline.
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