Course coordinator
Mr Richard Thomas
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Software systems are often composed of a heterogeneous network of inter-related systems. In this course you will build upon the knowledge and skills you have developed so far to learn how to design complex systems. This will include how these systems communicate and coordinate their responsibilities. You will learn design techniques to manage the complexity of large systems. You will learn how to assess and manage software risks (e.g.security, scalability, availability, resilience, robustness). You will apply these techniques to build a system composed of heterogeneous computing devices (e.g. mobile devices, servers, cloud-hosted services). You will learn how to apply systems thinking to design large-scale cyber-physical systems.
Large software systems are composed of interacting components. This is the case even if the system is only a single executable running on one computer. More sophisticated systems take advantage of the ability to distribute processing behaviour across hardware optimised for different parts of the system. In this course you will learn how to design "in the large". You will learn how to design components and their interfaces so that you can build complex systems. You will learn how to choose appropriate architectural patterns to structure systems that meet both functional and non-functional requirements of a system.
You can view this course as a capstone of your program. It integrates and extends the knowledge and skills you have developed so far.
Course Changes in Response to Previous Student Feedback
Assessment has been redesigned based on feedback and observation of student engagement with the course.
- The cloud infrastructure assignment has been staged to make progressive development towards the final solution easier.
- The presentation has been changed to be a critique of your team's project.
Practical work has been turned into a streamed example of building a cloud-based application.
Course material has been updated to provide better coverage of some topics.
Course requirements
Assumed background
You are assumed to have a strong background in software development. You are expected to have worked on at least one substantial software project, involving a team of members and thousands of lines of code. You should be able to design a software application consisting of several interoperating classes.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
CSSE3200 or DECO2800
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
CSSE3012 or CSSE3002
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures and practicals start in week 1.
The case study series starts in week 2.
Aims and outcomes
Upon successful completion of this course you will be able design complex software systems. You will understand how different architectural patterns support delivery of different design goals, and how to choose a pattern that is appropriate in your system's context. You will be able to evaluate the architecture of an existing system to assess how well it achieves its design goals. You will know how to apply security principles to deliver secure systems.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Design and build a software system that delivers specified functional and non-functional requirements (e.g. security, scalability, reliability, ...) by evaluating and selecting an appropriate architectural pattern and designing the components, interfaces and communication protocols to work within the pattern's structure.
LO2.
Apply systems thinking within a team, debating and negotiating options, to design an architecturally complex software system that delivers a coherent set of functional and non-functional requirements.
LO3.
Evaluate security risks for a software system architecture and implement security principles to minimise those risks.
LO4.
Select an appropriate suite of tools to facilitate the delivery of a large, complex software system.
LO5.
Describe and critique the architecture of a software system.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project |
Project Proposal
|
5% |
25/03/2025 3:00 pm |
| Computer Code, Project |
Cloud Infrastructure Assignment
|
35% |
API Functionality 4/04/2025 3:00 pm Deployed to Cloud 17/04/2025 3:00 pm Scalable Application 9/05/2025 3:00 pm |
| Presentation |
Architecture Presentation
|
25% |
26/05/2025 11:00 am
Your team must submit the slides that you will use for your presentation to the link provided on BlackBoard by the submission deadline. Your team will be allocated a timeslot in a class session during week 13 to make your presentation. A class session may be a lecture, practical, case study contact, or other specially scheduled session. You may request your team's presentation be scheduled to avoid a particular class session, to accommodate known circumstances. This request must be made to the course coordinator by the end of week 10. |
| Computer Code, Project |
Delivering Quality Attributes Project
|
35% |
9/06/2025 3:00 pm |
Assessment details
Project Proposal
- Online
- Mode
- Written
- Category
- Project
- Weight
- 5%
- Due date
25/03/2025 3:00 pm
- Learning outcomes
- L01, L03
Task description
For the course project you will design and deliver a minimal viable product (MVP) for a software system, demonstrating how it delivers selected quality attributes. This is the proposal phase of the project.
The proposal is an individual assessment. Everyone in the course will write their own proposal for a software system. You will describe a software system that you would like to implement. Use this as an opportunity to build something you have always wanted to do but have not had time. You will identify the functionality to be delivered by the MVP and the quality attributes of highest importance to the success of the system. You will also describe how you will evaluate that your delivered system achieves its functional and non-functional requirements.
You will review the proposals submitted by other course participants and vote on the ones you think are most interesting. Voting will determine which projects proceed to the development phase of the project. Votes are due by 15:00 (AEST) on April 4. Extensions are not possible for the voting as the results will be used to form teams after the submission deadline.
If you obtain an extension for submitting your project proposal, it may not be included in the voting process but will still be marked.
Artificial Intelligence (AI) and Machine Translation (MT)
AI and MT are emerging tools that may support you in completing this assessment task. You may appropriately use AI and/or MT in completing this assessment task. You must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. You must indicate any text or diagrams that were produced with the assistance of AI tools. The final section of your proposal should be an appendix indicating which specific sections or diagrams were AI or MT supported, and by which systems.
Submission guidelines
Details about how to submit your proposal, and how to vote on other proposals, will be described in the assignment specification.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
Cloud Infrastructure Assignment
- Online
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Project
- Weight
- 35%
- Due date
API Functionality 4/04/2025 3:00 pm
Deployed to Cloud 17/04/2025 3:00 pm
Scalable Application 9/05/2025 3:00 pm
- Learning outcomes
- L01, L04
Task description
You will implement a software system to meet specified functional and non-functional requirements. The implementation will be deployed on cloud infrastructure and stress tested. You are required to design the system and select the appropriate compute platforms to deliver specified requirements.
This assessment is to be delivered in three stages.
- API Functionality – Implement the specified API and its functionality but only running on a local computer.
- Deployed to Cloud – Deploy your implementation of the API and its functionality to cloud infrastructure.
- Scalable Application – Implement scalability so that the system responds to load demands in an economic fashion.
This exercise requires you to use cloud infrastructure from a specific provider. Credit will be available to complete the assignment on the provider's platform. Poor design choices may exceed your available credit. In some circumstances this may limit your maximum mark as you are not able to complete, or the teaching team is not able to assess, part of your assignment. The exercise is designed so that it can be completed well within your provided credit.
Artificial Intelligence (AI) and Machine Translation (MT)
AI and MT are emerging tools that may support you in completing this assessment task. You may appropriately use AI and/or MT in completing this assessment task. You must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. You must include with your submission a file AI.md that indicates the AI and MT tools that you used and the extent of their use. (e.g. All code was written by providing copilot with class descriptions and then revising the generated code. Classes A and B were produced by the following prompts to ChatGPT and were then adapted to work in the assignment's context.)
Submission guidelines
Submission requirements will be provided in the assignment specification.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students within 14 days.
Late submission
A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.
Architecture Presentation
- Identity Verified
- Team or group-based
- In-person
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 25%
- Due date
26/05/2025 11:00 am
Your team must submit the slides that you will use for your presentation to the link provided on BlackBoard by the submission deadline.
Your team will be allocated a timeslot in a class session during week 13 to make your presentation. A class session may be a lecture, practical, case study contact, or other specially scheduled session. You may request your team's presentation be scheduled to avoid a particular class session, to accommodate known circumstances. This request must be made to the course coordinator by the end of week 10.
- Learning outcomes
- L01, L02, L03, L05
Task description
Your team will present the details of your project's software architecture. You will critique the architecture in terms of how suited it is to delivery your project's key quality attributes. You will discuss security implications of your software architecture and design. You will describe alternative architectures that you considered and explain why they were not selected.
Each team member must present part of the team's presentation. They must answer at least one of the key questions listed in the assessment specification. Each team member will be awarded an individual grade for the presentation.
The result achieved by your presentation, before any penalty is applied, will be used to determine any cap on your final grade.
Artificial Intelligence (AI) and Machine Translation (MT)
This task has been designed to be challenging, authentic and complex. Whilst you may use AI and MT technologies, successful completion of assessment in this course will require you to critically engage in specific contexts and tasks for which AI will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. You must indicate any text or diagrams that were produced with the assistance of AI tools. Use a footnote indicating which AI system was used.
To pass this assessment you will be required to demonstrate detailed comprehension of the content of your slides, independent of AI and MT tools.
Submission guidelines
The presentation is an identity verified assignment. You must make your presentation in-person, and you must provide evidence of your identity (i.e. valid UQ student card) at the start of your presentation session.
In accordance with UQ Assessment Policy, your presentation will be recorded.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
This course uses team-based assessment where oral presentation sessions are scheduled with multiple markers and is time limited.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Presentations that are not made at their scheduled time will be subject to a late penalty of 100%. There is no grace period.
You are required to make your presentation during scheduled class times. To fit all students into the limited time available, presentations are scheduled for a fixed time. Missing your scheduled time means that rescheduling may disadvantage other students. Consequently, arriving late for your presentation timeslot will result in your presentation not being marked, and you will not be able to obtain a passing grade in the course.
Delivering Quality Attributes Project
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Project
- Weight
- 35%
- Due date
9/06/2025 3:00 pm
- Other conditions
- Peer assessment factor.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
You will design and deliver a minimal viable product (MVP) for a software system, demonstrating how it delivers selected quality attributes. This is the development phase of the project.
Project Development
You will be formed into teams to deliver the project described in a nominated proposal. As a team you will design and document the software architecture, implement, test and deliver the software system, and write a report describing the software architecture, and how the design delivers the specified functional and non-functional requirements.
Platform
This project does not require you to use any specific cloud infrastructure. Part of your design process is to identify appropriate computing platforms to use as the physical architecture of your system. Credit will be available to use on a nominated platform (e.g. AWS). Limitations will be applied on the services available to avoid excess costs being accrued. If you need to use a cloud-based platform, your team will need to decide how to make use of any credit that you have available between all the team members. Poor design choices may exceed your available credit. In some circumstances this may limit your maximum mark if you are not able to complete, or the teaching team is not able to assess, part of your project. In other circumstances this may lead to you paying extra commercial fees to the platform provider. Paying for extra services to boost your project's performance in some way will not improve the final result you achieve on the project.
Peer Assessment
Peer assessment of the quality and quantity of each team member's contributions to this project may be used to adjust individual results. Peer assessments will be moderated by the teaching team and their observations of individual's contributions during contact sessions will also be used if individual results are adjusted. Individual grades for the project may be increased or decreased.
Artificial Intelligence (AI) and Machine Translation (MT)
This task has been designed to be challenging, authentic and complex. Whilst you may use AI and MT technologies, successful completion of assessment in this course will require you to critically engage in specific contexts and tasks for which AI will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. You must include with your submission a file AI.md that indicates the AI and MT tools that you used and the extent of their use. (e.g. All code was written by providing copilot with class descriptions and then revising the generated code. Classes A and B were produced by the following prompts to ChatGPT and were then adapted to work in the assignment's context.)
To pass this assessment you will be required to demonstrate detailed comprehension of your submitted project artefacts, independent of AI and MT tools.
Submission guidelines
Submission will be via a git repository that will be provisioned for your team.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
This course uses team-based assessment of a project developed with regular formative feedback in practical classes. All work on the project is to be continuously submitted through git, from the start of the project. Consequently, there should always be a submittable version of the project by the deadline. For this reason, this assignment has ADA approval for no extensions or deferrals.
If teams encounter extraordinary difficulties in meeting a deadline, they should contact the course coordinator well in advance of the due date.
Late submission
100% Late Penalty after 1 hour grace period.
The one-hour grace period is recorded from the time the submission is due.
After the one-hour grace period, the work available in the git repository provisioned for the team will be marked. If no work is available in the repository, the team will receive a grade of X for the project, indicating no work submitted.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: To achieve a final grade of 1, the result of the formula in the Additional course grading information section is 1. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: To achieve a final grade of 2, the result of the formula in the Additional course grading information section is 2. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: To achieve a final grade of 3, the result of the formula in the Additional course grading information section is 3, AND your Presentation Result >= 3. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: To achieve a final grade of 4, the result of the formula in the Additional course grading information section is 4, AND your Presentation Result >= 4. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: To achieve a final grade of 5, the result of the formula in the Additional course grading information section is 5, AND your Presentation Result >= 5. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: To achieve a final grade of 6, the result of the formula in the Additional course grading information section is 6, AND your Presentation Result >= 5. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: To achieve a final grade of 7, the result of the formula in the Additional course grading information section is 7, AND your Presentation Result >= 5. |
Additional course grading information
This course uses a grade-based approach to evaluation. You will be awarded a grade, and not a mark, for each assessment activity. Your final grade is determined by the following formula.
Final Grade = ROUND(Project Proposal Gradeﾠ×ﾠ5% + Cloud Infrastructure Assignment Gradeﾠ×ﾠ35% + Architecture Presentation Gradeﾠ×ﾠ25% + Delivering Quality Attributes Project Gradeﾠ×ﾠ35%)
In the formula above, your final grade is rounded to the nearest integer (e.g. 4.45 is rounded to a grade of 4, 4.5 is rounded to a grade of 5).
Note that in the formula above, each assessment component (e.g. Project Proposal Grade) is an integer grade from 1 to 7 and not a fractional mark.
The presentation is an identity verified hurdle assessment. In order to obtain a final grade greater than
- 2, your presentation result must be at least 3.
- 3, your presentation result must be at least 4.
- 4, your presentation result must be at least 5.
Your presentation result is the grade you obtain for the presentation, before any penalty is applied to your presentation grade (e.g. before a penalty applied because of late submission of slides).
The course coordinator reserves the right to moderate results.
Supplementary assessment
Supplementary assessment is not available for some items in this course.
If your final grade is a 3, and your Delivering Quality Attributes Project grade is 3 or lower, you will not be eligible for supplementary assessment. The project activity includes group collaboration, and assesses the outcome of that collaboration, which cannot be replicated in supplementary assessment.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help. Speak to any member of the teaching team.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Course Material
The course website contains extensive lecture notes, lecture slides, and other course material. You are expected to read the lecture notes for a topic before the lecture that week. You are also expected to read the practical worksheets and case study briefs before your practical class and case study contact session.
All resources will be accessible from links on BlackBoard.
Tools
You will be required to create architectural diagrams for some learning and assessment activities. Structurizr Lite, Astah, PlantUML, and other modelling tools may be downloaded on your own computer. Some tools (e.g. Structurizr Cloud Service) may be used online. You will be given a free educational license for Structurizr Cloud Service, if you register using your UQ student email address.
Announcements will be distributed via Blackboard. You are expected to read Blackboard notices regularly, at least once per week and more frequently close to assessment deadlines.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lecture Series The lectures, including guest lectures, introduce the theory relevant to designing software architectures. Learning outcomes: L01, L02, L03, L04, L05 |
Practical |
Practical Computer Exercises The first part of semester will introduce technology to build complex, scalable systems. The remainder of the semester will be dedicated to project work. Learning outcomes: L01, L02, L03, L04, L05 |
|
Multiple weeks From Week 2 To Week 13 |
Case-based learning |
Case Study Series Each week we will explore software architecture options to implement different systems. Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: