MañanaList App: A Mobile To-Do List Application for Efficient and Personalized Task Management
This study developed the MañanaList App, a mobile to-do list application for efficient task management.” The research employed a developmental research design and adopted the Waterfall Software Development Life Cycle (SDLC), which provided a structured approach consisting of requirement analysis, system design, implementation, and testing. The system was evaluated through researcher-conducted internal testing, with each functional requirement translated into test cases that documented inputs, expected outputs, actual outputs, and results. In the ten functional test cases executed, all of the test cases passed successfully. Findings revealed that all core features, including login authentication, add task, modify task, delete task, mark as complete task, view pending task, view completed task, view menu, email notification-task added, and email notification-task due date, were implemented and validated. The notification feature reliably delivered confirmation emails for task creation and reminder emails for due tasks, ensuring that users remain informed of their responsibilities. The non-functional usability, performance, availability, security, and scalability requirements were also satisfied, confirming that the application is intuitive, responsive, secure, and capable of handling multiple tasks without performance degradation. Overall, the results affirm that the MañanaList App effectively fulfills its objective of providing a reliable and user-friendly tool for task organization and productivity. Recommendations for future work include expanding notification options, integrating offline functionality, enabling task categorization and prioritization, and conducting large-scale usability testing with end-users.
Authors
- Ma. Magdalena V. Gatdula
- Maylyn F. Torino
- Meshelle N. Fabro
- Chenaniah Keziah M. Pastrana
Publication Details
- Journal
- International Journal of Research and Innovation in Social Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.47772/ijriss.2025.909000215
- Primary Topic
- Scheduling and Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00