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 ?	[(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	No

Teaching staff

Coordinator	Basem Suleiman, basem.suleiman@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Individual contribution reports Individual contribution to the group project (W3, W6, W9, W12)	25%	Multiple weeks	multiple weeks
Outcomes assessed:
Presentation	First project demo and presentation Presentation & demo of first set of requirements.	5%	Week 06	in-tutorial
Outcomes assessed:
Assignment	First project report (group) first group report of completed work	10%	Week 06	multiple weeks.
Outcomes assessed:
Presentation	First client deployment deploy & demo first product at client site.	5%	Week 07	week 7
Outcomes assessed:
Presentation	Final product delivery to client Complete product delivery & demo at the client site.	10%	Week 12	week 12
Outcomes assessed:
Presentation	Final demo and presentation Presentation & demo of the final product.	10%	Week 12	in-tutorial
Outcomes assessed:
Assignment	Final project report (group)	35%	Week 12	multiple weeks
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

First project 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.
First project report (group): 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.
Individual contribution reports (4 individual reports)*: focuses on the individual contributions to the group project work completed by each student.All students will be required to give some feedback about the other team members of their group, in order to validate the individual reports as well.
First client deployment: first set of test and implemented requirements must be deployed at client site. Students need to demonstrate that 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 (group): this report discusses important aspects including technical details of the final delivered product, how it met client’s needs, approach followed 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.

* The marks obtained from this assessemnt will be used to scale the total marks obtained from the group assessments as follows: final mark = total individual contribution marks + (total marks obtained from group assessments * percentage of individual contribution 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.

***In order to pass this unit, you must obtain at least 50% on indivudal assessment, and at least 50% overall!

For more information see guide to grades.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week -01	Unit overview, team formation, presentation/proposals of projects offered, collection of teams preferences;	Presentation (4 hr)
Week 02	1. Tutor meet all managers & tutor meets each group; 2. Group work and big 5 theory for efficient team work; 3. Group contract, XP, group management tools & first meeting with client	Project (2 hr)
Week 03	1. XP and test-first; 2. set of requirements (user stories) defined, XP roles, demo of group's online progress; 3. Tutor meet all managers & tutor meets each group. 4. progress with new/existing requirements (user stories) implementation and testing.	Project (2 hr)
Week 04	1. Workload management and awareness of teamwork; 2. Working towards first demo and report; 3. Tutor meet all managers & tutor meets each group 4. prototype implementation and testing	Project (2 hr)
Week 05	1. Continue working towards first prototype, demo and report; 2. Tutor meet all managers & tutor meets each group 3. prototype implementation and testing 4. more/refine requirements	Project (2 hr)
Week 06	1. First presentation & demo and reports by all groups. 2. preparation for first client deployment & demos.	Project (2 hr)
Week 07	1. continue working on new user stories/requirements 2. Preparation for first client demo. at client site 3. implementation of other requirements 4.Tutor meet all managers & tutor meets each group	Project (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 meet all managers & tutor meets each group to discuss feedback on first reports, development progress & first client deployment follow ups.	Project (2 hr)
Week 09	1. code refactoring and progress with requirements (user stories) 2. implementation & testing of requirements (user stories) 3. 2. Advanced testing types; 4. Tutor meet all managers & tutor meets each group to discuss development progress, client's feedback and interactions	Project (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	Project (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. planning for final product delivery & demo.	Project (2 hr)
Week 12	1. final group demo and presentation in-tutorials 2.final product demo and delivery 3. complete project work and documentation 4. discuss final assessment with tutors	Project (2 hr)
Week 13	1. follow ups on final assessments 2. follow ups on client's final product demo and delivery 3. completion of project work and documentation where needed 4. Tutor meet all managers & tutor meets each group to discuss development progress	Project (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 report of student’s work, in style that conforms to their discipline and is appropriate for clients to check.
LO2. Give an oral presentation of your work and its rationale to your discipline.
LO3. Work in a small group of students, to deliver substantial results for clients.
LO4. Follow a systematic process of work, with appropriate mechanisms for dealing with 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. Work with clients to discover their needs and to 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

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

COMP3988: Computer Science Project (Advanced)

Semester 2, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect