Week 8 Worklog

05/06/2026

Work Completed

  • Organized role assignments and responsibilities for each team member based on their major and strengths.
  • Defined each member’s role in the LiveCap project: Information Systems handles BA/SA and data management; Networking handles cloud infrastructure and security; Machine Learning handles speech and language processing; Software Engineering handles frontend, backend, WebSocket, and testing.
  • Clarified responsibility boundaries between roles to avoid overlapping work during implementation.
  • Identified key deliverables for each role, including SRS, use cases, BPMN/activity diagrams, ERD, KPIs, infrastructure design, speech-to-text pipeline, and test suite.

Results Achieved

  • The team had a clear work assignment structure before starting project implementation.
  • Each member understood their main responsibilities, scope, and required deliverables.
  • The Information Systems role was defined to focus on requirements analysis, process design, data modeling, KPIs, UAT, and system documentation.
  • Established a foundation for effective collaboration between frontend, backend, AI processing, and cloud infrastructure.

06/06/2026

Work Completed

  • Analyzed the LiveCap problem in detail from both the user and system perspectives.
  • Identified the main user groups: students, workshop attendees, technical content learners, and those who need to follow bilingual content.
  • Drafted functional requirements: start session, record audio, receive captions, translate content, save transcript, and export file.
  • Drafted non-functional requirements: low latency, stable connection, data security, scalability, and browser usability.

Results Achieved

  • Completed the initial system requirements draft for LiveCap.
  • Clarified what the system needs to do, how users interact with it, and what outputs are expected.
  • Established a foundation for developing the SRS document and refining the MVP scope.
  • Prevented scope creep beyond the core goal of real-time captioning, translation, and transcript export.

07/06/2026

Work Completed

  • Designed the main business process flow for LiveCap: user starts session → system records audio → sends audio stream → receives captions → translates content → displays in real time → exports transcript.
  • Drafted use cases for the system’s core features.
  • Designed an initial data model for the key entities: session, transcript segment, speaker, translation, and export file.
  • Identified the data fields required per session: start time, end time, language, transcript content, translation, and export status.

Results Achieved

  • Produced a description of the system’s main operational process.
  • Identified the key use cases within the MVP.
  • Produced an initial data model to support transcript storage and export.
  • Further clarified the Information Systems member’s role in system logic design and data management.

08/06/2026

Work Completed

  • Discussed the initial technical architecture of LiveCap as a team.
  • Identified the main system components: frontend, WebSocket backend, Amazon Transcribe Streaming, Amazon Translate, Amazon S3, and Amazon CloudWatch.
  • Analyzed the real-time data flow: microphone → frontend → WebSocket backend → Amazon Transcribe → Amazon Translate → frontend displays captions and translations.
  • Discussed the approach to browser-side audio processing and the bilingual display interface for users.

Results Achieved

  • Finalized the high-level architecture for LiveCap’s MVP.
  • The team agreed that the backend needs to handle real-time communication via WebSocket.
  • Clarified the role of each AWS service: Transcribe Streaming for speech-to-text, Translate for bilingual translation, S3 for transcript export storage, and CloudWatch for logging and monitoring.
  • Established a technical foundation to begin prototype development.

09/06/2026

Work Completed

  • Started preparing the development environment for the project.
  • The Software Engineering member began setting up the frontend structure with React/Vite and the backend with FastAPI.
  • Discussed how the backend should manage the session lifecycle: create session, maintain connection, receive audio stream, and end session.
  • Identified the key UI states to handle: loading, microphone permission denied, connecting, connected, reconnecting, and error.
  • The Information Systems member continued updating the requirements document based on the team’s technical design.

Results Achieved

  • Established a clearer implementation direction for both frontend and backend.
  • Identified the key states that matter for user experience.
  • Clarified how the system handles a full caption session from start to finish.
  • Updated the requirements document to better reflect real-world implementation constraints.

10/06/2026

Work Completed

  • Defined the plan for integrating AWS services into the prototype.
  • The Machine Learning member researched Amazon Transcribe Streaming configuration, audio format requirements, partial transcripts, and final transcripts.
  • The Networking member researched required infrastructure components: EC2, S3, CloudFront, security groups, HTTPS/TLS, and WebSocket Secure.
  • The Information Systems member defined the KPIs to track: caption latency, transcript accuracy, session duration, error rate, and export status.
  • Discussed initial testing criteria for UAT and system testing.

Results Achieved

  • The team had a clearer integration plan across frontend, backend, AI services, and AWS infrastructure.
  • Identified the key KPIs for evaluating system quality.
  • Established initial testing directions for core features: starting a session, receiving captions, translating content, and exporting transcripts.
  • Confirmed that the team would use AWS managed services for speech-to-text and translation rather than training custom AI models.

11/06/2026

Work Completed

  • Reviewed the overall progress from the first week of team-based project implementation.
  • Verified alignment between system requirements, technical architecture, each member’s role, and end-of-term deliverables.
  • Added technical risks to monitor: real-time latency, Vietnamese/English recognition quality, microphone errors, WebSocket disconnection, and AWS costs.
  • Updated system documentation: functional requirements, non-functional requirements, use cases, data model, KPIs, and UAT plan.
  • Prepared the plan for the following week: begin prototype implementation, test the real-time caption flow, and finalize key integration components.

Results Achieved

  • Completed the team organization and initial system design phase for LiveCap.
  • Produced a foundational set of documentation to support implementation and project reporting.
  • The team reached consensus on the MVP implementation direction, functional scope, and risks to manage.
  • Ready to move into a more intensive implementation phase in the following week.

Weekly Summary

This week, the team transitioned from the ideation phase into team organization and system design for LiveCap. Assigning clear roles to the four members — Information Systems, Networking, Machine Learning, and Software Engineering — made it clear who is responsible for requirements analysis, cloud infrastructure, speech and language processing, and software development. Through the Information Systems role, I learned how to analyze a project not just technically, but also from a business process and operational perspective — covering problem statement, functional and non-functional requirements, use cases, data modeling, KPIs, and UAT planning. By the end of the week, the team had a solid documentation and architecture foundation to begin prototype development in the coming weeks.