Outline

Overview

This unit of study will focus on the design, the architecture and the development of web applications using technologies currently popular in the marketplace including Java and . NET environments. There are three key themes examined in the unit: Presentation layer, Persistence layer, and Interoperability. The unit will examine practical technologies such as JSP and Servlets, the model-view-controller (MVC) architecture, database programming with ADO. NET and JDBC, advanced persistence using ORM, XML for interoperability, and XML-based SOAP services and Ajax, in support of the theoretical themes identified. On completion the students should be able to: Compare Java/J2EE web application development with Microsoft . NET web application development; Exposure to relevant developer tools (e. g. Eclipse and VS. NET); Be able to develop a real application on one of those environments; Use XML to implement simple web services and AJAX applications.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	(INFO1103 or INFO1110 or INFO1910 or ENGG1810) and (INFO2110 or ISYS2110) and (INFO2120 or INFO2820 or ISYS2120)
Corequisites ?	None
Prohibitions ?	EBUS4001
Assumed knowledge ?	None
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.	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Creative work	Individual task 1 See Canvas for more details.	1%	Week 02	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 2 See Canvas for more details.	1%	Week 03	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 3 See Canvas for more details.	1%	Week 04	n/a	AI allowed
Outcomes assessed:
Creative work	System and Database Design See Canvas for more details.	15%	Week 06	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 4 See Canvas for more details.	1%	Week 06	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 5 See Canvas for more details.	1%	Week 07	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 6 See Canvas for more details.	1%	Week 08	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 7 See Canvas for more details.	1%	Week 09	n/a	AI allowed
Outcomes assessed:
Creative work	Individual task 8 See Canvas for more details.	1%	Week 10	n/a	AI allowed
Outcomes assessed:
Creative work	System implementation & testing See Canvas for more details.	35%	Week 11	n/a	AI allowed
Outcomes assessed:
Creative work	System deployment and security See Canvas for more details.	12%	Week 13	n/a	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

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.

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	1. Introduction; 2. Course mechanics; 3. Web fundamentals	Lecture (1 hr)
	01 Project Overview & System/Database Design	Individual study (3 hr)
	Intro review and preparation, covering topics in reading material and online resources	Individual study (6 hr)
Week 02	1. Web fundamentals (continued); 2. Use case modelling; 3. Wireframing; 4. Requirements analysis and requirements specification for web applications	Lecture (1 hr)
	02 RESTful API & Git/Python/Docker Recap	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 03	Web servers and application architecture	Lecture (1 hr)
	03 Intro to Django & MVT in Django	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 04	Front-end web technologies and development environments	Lecture (1 hr)
	04 Database in Django & Django Advance 1	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 05	1. Deliverable: project pitches; 2. Back-end web technologies, methods, and security	Lecture (1 hr)
	A1 Milestone Presentation	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 06	1. Deliverable: project document; 2. Deploying, configuring, and securing pre-packaged software	Lecture (1 hr)
	05 AWS Cloud & GraphQL	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 07	Cloud services and deployment	Lecture (1 hr)
	06 Django REST Framework & Django Advance 2	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 08	Web security introduction/overview and policies	Lecture (1 hr)
	07 Cookie, Session, Django Authentication & Backend Communication Mechanisms	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 09	Quality, Testing and Validation	Lecture (1 hr)
	08 System Security	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 10	Continuous integration and testing	Lecture (1 hr)
	09 System Deployment & Linux	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Individual study (6 hr)
Week 11	1. Deliverable: Working system demo; 2. Guest Lecture	Lecture (1 hr)
	A2 Project Demo	Computer laboratory (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 12	Guest Lecture	Lecture (1 hr)
	A2 Tech Interview	Block teaching (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)
Week 13	Exam review	Lecture (1 hr)
	A3 Tech Interview	Block teaching (3 hr)
	Material reading and knowledge review, covering topics in lec&lab	Independent study (6 hr)

Attendance and class requirements

Project Work - own time: The project requires students to design and develop web services. It involves group meetings, discussions, and development sessions.

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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Anders Moller and Michael Schwartzbach, An Introduction to XML and Web Technologies. Addison-Wesley, 2006. 0321269667.
Dean Leffingwell, Don Widrig, Managing Software Requirements: A Use Case Approach. Addison-Wesley, 2003. 032112247X.
Geoff Hulten, Building intelligent systems: a guide to machine learning engineering. Apress, 2018. 2018934680.
Chip Huyen, Designing machine learning systems: an iterative process for production-ready applications. O’Reilly, 2022. 1098107969.

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. Use Python and Django framework as the baseline programming tools.
LO2. Develop web based services from inception to design through to implementation, testing, and maintenance by using principles, techniques, and methodologies presented
LO3. Use tools and methods employed in web service design, implementation, and testing to the extent of the material and projects presented.
LO4. Develop real world web applications using web-based environments and the principles and techniques presented in the course
LO5. Deploy applications by using basic cloud services and DevOps practices with baseline security measures.
LO6. Instigate inquiry and knowledge development into the issues associated with designing and building a web based service, and synthesise the information to draw meaningful and useful conclusions in the context of the subject at hand
LO7. Work in a team and assuming different roles (stakeholders), while remaining receptive to other inputs and opinions, so as to deliver real-world web applications on time, and within scope
LO8. Proficiently write reports that convey complex and technical concepts, experiments, and present outcomes on web services projects in a clear and concise form
LO9. Demonstrate ethical and professional responsibility in software engineering practice, including awareness of privacy, legal issues, accessibility, and the societal impact of technology.

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.

Update the unit with lab changes and assessments restructure.

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

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC3609: Internet Software Platforms

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

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links