Outline

Overview

This unit will provide students an opportunity to apply the knowledge and practise the skills acquired in the prerequisite and qualifying units, in the context of designing and building a substantial software development system in diverse application domains including life sciences. Working in groups for an external client combined with academic supervision, students will need to carry out the full range of activities including requirements capture, analysis and design, coding, testing and documentation. Students will use the XP methodology and make use of professional tools for the management of their project.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(INFO1110 or INFO1910) and INFO1113 and (COMP2123 or COMP2823) and COMP2017 and (COMP2022 or COMP2922) with Distinction level results in at least one of these units
Corequisites ?	None
Prohibitions ?	INFO3600 or COMP3615 or COMP3600 or COMP3888
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Xiu Wang, xiu.wang@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Practical skill	Weekly Project Activities Supervision and Progress Engagement *week 2 & week 3 activities and performance are EFTs	7%	Ongoing Due date: 16 Aug 2026 at 23:59 Closing date: 06 Nov 2026	W2-W12 (inclusive), on going	AI allowed
Outcomes assessed:
Interactive oral	Project Proposal and Role Allocation Presentation in-tutorial presentation Project Proposal and Scope Statement Submission (group)	5%	Week 03 Due date: 23 Aug 2026 at 23:59 Closing date: 04 Sep 2026	In-tutorial	AI prohibited
Outcomes assessed:
Written work	Project Proposal and Scope Statement group work	5%	Week 03 Due date: 23 Aug 2026 at 23:59 Closing date: 04 Sep 2026	week 3	AI allowed
Outcomes assessed:
Interactive oral	Project progress demo and presentation Presentation & demo of project progress	8%	Week 06 Due date: 13 Sep 2026 at 23:59 Closing date: 25 Sep 2026	in-tutorial	AI prohibited
Outcomes assessed:
Written work	Project Progress Report Project progress report	10%	Week 06 Due date: 13 Sep 2026 at 23:59 Closing date: 25 Sep 2026	multiple weeks	AI allowed
Outcomes assessed:
Peer or expert observation or supervision	Project progress and product deployment at client side deploy & demo of project progress and product at client site.	5%	Week 07 Due date: 20 Sep 2026 at 23:59 Closing date: 25 Sep 2026	week 7	AI prohibited
Outcomes assessed:
Interactive oral	Final project demo and presentation Presentation & demo of the final product.	10%	Week 12 Due date: 01 Nov 2026 at 23:59 Closing date: 06 Nov 2026	in-tutorial	AI prohibited
Outcomes assessed:
Peer or expert observation or supervision	Final product delivery to client Complete product delivery & demo at the client site, project sign off and conclude.	10%	Week 12 Due date: 30 Oct 2026 at 23:59 Closing date: 07 Nov 2026	week 12	AI prohibited
Outcomes assessed:
Written work	Final project report (group) Final project report (group)	40%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 13 Nov 2026	multiple weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Assessment summary

Project proposal and scope statement, and presentation: based on consensus agreement with the client, clearly define the background, aims/objectives, success criteria, scope, milestones, resources and deliverables of the project.
Project progress demo and presentation: present the work done to understand the client’s requirements and complete the first set of requirements. This should be a major release, with some working and useful outcomes for the client and some parts presented in proposed form such as storyboards.
Project progress report: reports the work done to understand the client’s requirements and complete the first set of requirements. It should also report on the group work and processes followed, systems and software tools used, and client interaction.
Project progress demo and deployment to client: first set of test and implemented requirements must be deployed at client site. Students need to demonstrate that the group has had a professional approach to the project deployment and worked to ensure that the client will be satisfied. The client will be consulted to ratify the deployment.
Final delivery to client: final product and relevant documentation delivered to the client. This includes all the sources, necessary technical documentation and any product or documentation that the client required at the start of the project.
Final project demo and presentation: must provide a convincing demonstration that the group has performed a substantial amount of work that meets client needs and have had a professional approach to achieving that.
Final project report: must cover important aspects including technical details of the final delivered product, how it meets client’s needs, approaches to interact with the client, group’s discipline knowledge and skills used to tackle problems, the quality of the processes followed and the effectiveness of group management processes.
Self- and peer assessments and feedbacks from SparkPlus will be used to scale the group marks to calculate the final individual marks.

Detailed information for each assessment will be communicated on Canvas through the tutorials and Ed discussion forum.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

***Final assignments act similarly to the final exam in other subjects. In order to pass this unit, you must obtain at least 40% on final assessments, and at least 50% overall.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policies.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Unit overview, team formation, presentation/proposals of projects offered, collection of teams preferences;	Lecture (3 hr)
Week 01	Group formation, registration and group tag allocation; selection and submission of 5 group project preferences	Workshop (2 hr)
Week 02	1. Tutor meets all managers & tutor meets each group; 2. Clarification on Tasks; 3. Group work and big 5 theory for efficient team work; 4. Group contract, XP, group management tools & group's first meeting with client	Workshop (2 hr)
Week 03	1. Discuss progress in terms of classic XP elements; 2. Tutor meets all managers & tutor meets each group; 3. progress with new/existing requirements (user stories) implementation and testing; 4. submission of student Deed Poll, contract, and project scope statement.	Workshop (2 hr)
Week 04	1. In-tutorial proposal and role allocation presentation; 2.Workload management and awareness of teamwork; 3. Tutor meet all managers & tutor meets each group 4. prototype implementation and testing.	Workshop (2 hr)
Week 05	1. 1st status checking; 2. Tutor meets all managers & tutor meets each group 3. prototype implementation and testing 4. more/refine requirements; 5. Working towards the first assignment demo and report.	Workshop (2 hr)
Week 06	1. continue working on new user stories/requirements; 2. Preparation for the project progress report, demo, and presentation 3. implementation of other requirements; 4.Tutor meet all managers & tutor meets each group.	Workshop (2 hr)
Week 07	1. Project progress presentation & demo and reports by all groups. 2. preparation for first client deployment & demos.	Workshop (2 hr)
Week 08	1. progress with requirements (user stories); implements & test requirements 2. Code refactoring, more tests (user acceptance tests); 3. implementation of requirements (user stories); 4. Tutor meets all managers & tutor meets each group to discuss feedback on first assignment, development progress & first client deployment follow ups.	Workshop (2 hr)
Week 09	1. 2nd status checking 2. progress with requirements (user stories); implements & test requirements 3. Code refactoring, more tests (user acceptance tests); 4. implementation of requirements (user stories); 5. Tutor meets all managers & tutor meets each group member.	Workshop (2 hr)
Week 10	1. code refactoring and progress with requirements (user stories); implementation & testing of requirements (user stories) 2. Advanced testing types 3. Tutor meet all managers & tutor meets each group to discuss development progress 4. initial planning for final client deployment.	Workshop (2 hr)
Week 11	1. code refactoring and progress with requirements (user stories); implementation & testing of requirements (user stories) 2.Tutor meet all managers & tutor meets each group to discuss development progress; 3. Discuss and prepare for the final assignment; 4. planning for final product delivery & demo.	Workshop (2 hr)
Week 12	1. prepare final assignment group demo and presentation in-tutorials 2.final product demo and delivery at client site; 3. work on Final Group Report.	Workshop (2 hr)
Week 13	1.in-tutorial final project demo and presentation 2. Concluding remarks and feedback; 3. follow ups on final assessments; 4. follow ups on client's final product demo and delivery; 5. completion of project work and documentation where needed; 6. Tutor meets all managers & tutor meets each group to discuss feedback.	Workshop (2 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Produce written reports of student’s work that aligns with their discipline standards and is professional for project delivery.
LO2. Demonstrate professional presentation skills through oral presentations and demonstrations of your work and the rationale to your discipline.
LO3. Demonstrate skills of working with their peers in a small group and the clients to deliver substantial outputs for clients.
LO4. Follow a systematic process of work, with appropriate mechanisms for mitigating risks, such as unforeseen factors and delays.
LO5. Use previously unfamiliar tools and to integrate with previously unfamiliar software/systems, learning their use from examples and documentation.
LO6. Demonstrate analytic and problem solving skills through working with clients to discover their needs, and demonstrate that the delivered results meet those needs
LO7. Apply discipline expertise in computer science to solve complex problems and meet the needs of clients.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Improved assessments of individual contribution to the group work and knowledge and skills of specific discipline

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP3988: Computer Science Project (Advanced)

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links