Lecture: 1 hour/week
and
Lab: 3 hours/week
Most of the instruction will be on a one–to–group basis between students and the faculty advisor to guide the students through a self–managed work plan. Weekly communication with the instructor will be compulsory.
Students must continue working on and finish the same project selected in the preceding course, CMPT 4001.
- Implementation sprints
- Implementation of prototypes or experimental setups based on design specifications
- Application of software engineering and performance optimization practices in implementation
- Code review, peer programming, or research validation sessions
- Integrating experimental methods (e.g., hypothesis testing, algorithm benchmarking) where applicable
- Quality testing
- Unit, integration, and system testing
- Automated testing frameworks and data validation pipelines for system or research outputs
- Test results analysis and defects resolution
- Deployment
- Configuring deployment environments
- Release management, rollback strategies, and/or replication protocols for research outputs
- Simulated deployment using industry-standard practices
- Documentation set
- Creating effective user manuals, troubleshooting guides, and research methodology documents
- Maintenance and sustainability plans
- Dissemination
- Creating a public-facing project website or repository
- Designing effective live demonstrations or poster presentations
- Post-mortem and impact analysis
- Comparison of project outcomes against initial key performance indicators (KPIs), research hypothesis, and objectives
- Documenting lessons learned, methodology improvements, and future enhancements or research directions
- Societal and ethical impact assessment
Upon successful completion of this course, students will be able to:
- construct a fully functional computing solution that implements the instructor-approved design from CMPT 4001 and meets specified requirements or research objectives;
- implement software or hardware quality assurance best practices by executing verification and validation tests on a computing solution and interpreting results to ensure reliability;
- produce comprehensive technical documentation and prepare user and operational guides for system deployment;
- present project outcomes effectively to technical and non-technical audiences using professional communication standards;
- critique project performance against initial objectives and propose improvements or future enhancements based on post-mortem analysis;
- reflect on societal, ethical, and legal implications of the developed solution and recommend strategies for responsible computing practice;
- apply principles of Indigenous data sovereignty and ethical technology use during implementation, testing, and dissemination, ensuring that project outcomes respect Indigenous knowledge systems and community priorities.
Assessment will be in accordance with the Douglas College Evaluation Policy. The instructor will present a written course outline with specific evaluation criteria at the beginning of the semester. This is a letter-graded course.
Evaluation will be based on the following:
|
Assignments |
0-10% |
|
Progress reports (minimum of two) |
5-25% |
|
Verification and validation evidence |
5-25% |
|
Documentation set |
5-25% |
|
Final project report and post-mortem analysis |
15-35% |
|
Final presentation and prototype demonstration |
10-25% |
|
Total |
100% |
Consult the Douglas College Bookstore for the latest required textbooks and materials.
None