Outline

Overview

This unit aims to introduce students to the main issues involved in producing large Internet systems by using and building application frameworks. Frameworks allow great reuse so developers do not have to design and implement applications from scratch, as students have done in ELEC3610 The unit lays down the basic concepts and hands on experience on the design and development of enterprise systems, emphasizing the development of systems using design patterns and application frameworks. A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. Several development Java frameworks will be used, including Spring, Hibernate, and others. Principles of design patterns will also be studied.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Java programming, and some web development experience are essential. Databases strongly recommended
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Huaming Chen, huaming.chen@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam See Canvas for more details.	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Performance	Sprint 0 See Canvas for more details.	2.5%	Week 03	n/a	AI allowed
Outcomes assessed:
Performance	Sprint 1 See Canvas for more details.	2.5%	Week 05	n/a	AI allowed
Outcomes assessed:
Written work	Proposal See Canvas for more details.	10%	Week 06	2000 words	AI allowed
Outcomes assessed:
In-class quiz	Mid-term quiz See Canvas for more details.	10%	Week 07	60 minutes	AI allowed
Outcomes assessed:
Performance	Sprint 2 See Canvas for more details.	2.5%	Week 08	n/a	AI allowed
Outcomes assessed:
Performance	Sprint 3 See Canvas for more details.	2.5%	Week 11	n/a	AI allowed
Outcomes assessed:
Written work	Project See Canvas for more details.	30%	Week 13	n/a	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Project: Prototype, final application, presentation, learning journal.
Mid-sem exam: Concepts and programming languages used in the project.
Proposal – draft: 1,500 words describing project.
Proposal – final: 2,000 word document describing the project, including technical solution and addressing feedback.
Presentation: Produce short group video describing project outcomes.

Detailed information for each assessment can be found on Canvas.

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.

For more information see sydney.edu.au/students/guide-to-grades.

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:

Generally 20% penalty marks per day. Special cases will be considered.

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	Introduction	Lecture (1 hr)
Week 01	Intro review and preparation, covering topics in reading material and online resources	Independent study (6 hr)
Week 02	Software Requirement and Feasibility Analysis	Lecture (1 hr)
	Project Selection and Group Formation	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 03	Object-Oriented Analysis	Lecture (1 hr)
	Project proposal; Sprint check	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 04	Object-Oriented Design - 1	Lecture (1 hr)
	Intro to Web Architecture and API Design	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 05	Object-Oriented Design - 2	Lecture (1 hr)
	System Design and Code Quality; Sprint check	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 06	Spring Boot and Microservices	Lecture (1 hr)
	Full-stack development; System implementation	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 07	Spring Boot 2; Data management	Lecture (1 hr)
	Progress Review and Concept Reinforcement; Mid-term quiz	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 08	Security Fundamentals; Spring Security	Lecture (1 hr)
	Spring security; Sprint check	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 09	Testing & QA	Lecture (1 hr)
Week 09	Group project, Material reading, and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 10	DevOps & Cloud	Lecture (1 hr)
	Containerisation & Cloud Deployment	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 11	Industry guest lecture	Lecture (1 hr)
	Sprint check	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 12	Industry guest lecture	Lecture (1 hr)
	Final Implementation Q&A; Submission Preparation	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 13	Course overview	Lecture (1 hr)
	Project presentation	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 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. Compare modern web development platforms using the principles and techniques developed in the course
LO2. Investigate, design, and document a web application based on user needs, using various information resources, feasibility analysis, and system modelling techniques.
LO3. Deploy a large complex web application using a specific modern application framework to the extent of the material presented
LO4. Evaluate the benefits and trade-offs of using domain specific software frameworks and design patterns for web development.
LO5. Customize software frameworks and development tools to meet changing user requirements through tools, principles and techniques developed throughout the course.
LO6. Work effectively in a group environment by taking responsibility for defined roles, and then interacting in a constructive manner with the group by both contributing and evaluating others' viewpoints in order to reach a multilateral agreement on and execution of the solution
LO7. Demonstrate ethical and professional responsibility in software engineering practice, including awareness of privacy, legal issues, accessibility, and the societal impact of technology.
LO8. Communicate technical and often complex material clearly and concisely in reports and presentations tailored to technical and non-technical audiences.
LO9. Identify and mitigate common security threats and vulnerabilities in web applications, especially in object-oriented application frameworks.
LO10. Assess and apply quality assurance techniques, testing strategies, and deployment pipelines to ensure software reliability.

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.

We have substantially updated the course content according to student feedback.

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

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

ELEC5619: Object Oriented Application Frameworks

Semester 2, 2025 [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