ELEC3609: Semester 2, 2022

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Units ELEC3609 Semester 2 2022 [Normal day]

Unit outline_

ELEC3609: Internet Software Platforms

Semester 2, 2022 [Normal day] - Remote

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) 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
Lecturer(s)	Huaming Chen, huaming.chen@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam Final exam	30%	Formal exam period	3 hours
Outcomes assessed:
Assignment	System requirements analysis n/a	12%	Week 05	n/a
Outcomes assessed:
Assignment	System design specification n/a	8%	Week 07	n/a
Outcomes assessed:
Assignment	System implementation n/a	35%	Week 11	n/a
Outcomes assessed:
Assignment	System deployment and testing n/a	15%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type D final exam ?

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 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.

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	1. Introduction; 2. Course mechanics; 3. Web fundamentals	Lecture and tutorial (4 hr)
Week 02	1. Web fundamentals (continued); 2. Use case modelling; 3. Wireframing; 4. Requirements analysis and requirements specification for web applications	Lecture and tutorial (4 hr)
Week 03	Web servers and application architecture	Lecture and tutorial (4 hr)
Week 04	Front-end web technologies and development environments	Lecture and tutorial (4 hr)
Week 05	1. Deliverable: 5 minute pitches and requirements document; 2. Back-end web technologies, methods, and security	Lecture and tutorial (4 hr)
Week 06	Deploying, configuring, and securing pre-packaged software	Lecture and tutorial (4 hr)
Week 07	1. Deliverable: System design specification; 2. Cloud services and deployment	Lecture and tutorial (4 hr)
Week 08	Web security introduction/overview and policies	Lecture and tutorial (4 hr)
Week 09	Quality, Testing and Validation	Lecture and tutorial (4 hr)
Week 10	Continuous integration and testing	Lecture and tutorial (4 hr)
Week 11	Data-driven web services for decision making	Lecture and tutorial (4 hr)
Week 12	Machine learning based systems: fundamentals, tools and practice	Lecture and tutorial (4 hr)
Week 13	1. Deliverable: Working system demos; 2. Exam review	Lecture and tutorial (4 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. 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
LO2. 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
LO3. Proficiently write reports that convey complex and technical concepts, experiments, and present outcomes on web services projects in a clear and concise form
LO4. Develop web based services from inception to design through to implementation, testing, and maintenance by using principles, techniques, and methodologies presented
LO5. Develop real world web applications using web-based environments and the principles and techniques presented in the course
LO6. Use tools and methods employed in web service design, implementation, and testing to the extent of the material and projects presented.
LO7. Use Python and Django framework as the baseline programming tools.
LO8. Deploy applications by using basic cloud services and DevOps practices with baseline security measures.
LO9. Demonstrate an understanding of current trends of web based services and applications using machine learning and artificial intelligence.

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.

Changed the assessment for COVID-19 requirement. Modified learning outcomes a bit.

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

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

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