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Unit outline_

ELEC9609: Internet Software Platforms

Semester 2, 2022 [Normal day] - Remote

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
? 
None
Corequisites
? 
None
Prohibitions
? 
ELEC5742
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

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) Type D final exam Final exam
Final exam
30% Formal exam period 3 hours
Outcomes assessed: LO5 LO6 LO7 LO8
Assignment group assignment System requirements analysis
n/a
12% Week 05
Due date: 04 Sep 2022 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment System design specification
n/a
8% Week 07 n/a
Outcomes assessed: LO1 LO5 LO3 LO2
Assignment group assignment System implementation
n/a
35% Week 11 n/a
Outcomes assessed: LO1 LO8 LO7 LO5 LO4 LO3
Assignment group assignment System deployment and testing
n/a
15% Week 12 n/a
Outcomes assessed: LO1 LO9 LO6 LO4
group assignment = group assignment ?
Type D final exam = Type D final exam ?

Assessment summary

  • Project: This project will involve 4 deliverables: Deliverable 1 (due week 5) covers response to request for proposal with requirements analysis, and specification to web services, deliverable 2 (due week 7) covers design specifications of web services, deliverable 3 (due week 12) covers implementation of web applications, and deliverble 4 (due week 13) covers test results and user documentation.

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Introduction; 2. Course mechanics; 3. Web fundamentals Online class (4 hr)  
Week 02 1. Web fundamentals (continued); 2. Use case modelling; 3. Wireframing; 4. Requirements analysis and requirements specification for web applications Online class (4 hr)  
Week 03 Web servers and application architecture Online class (4 hr)  
Week 04 Front-end web technologies and development environments Online class (4 hr)  
Week 05 1. Deliverable: 5 minute pitches and requirements document; 2. Back-end web technologies, methods, and security Online class (4 hr)  
Week 06 Deploying, configuring, and securing pre-packaged software Online class (4 hr)  
Week 07 1. Deliverable: System design specification; 2. Cloud services and deployment Online class (4 hr)  
Week 08 Web security introduction/overview and policies Online class (4 hr)  
Week 09 Quality, Testing and Validation Online class (4 hr)  
Week 10 Continuous integration and testing Online class (4 hr)  
Week 11 Data-driven web services for decision making Online class (4 hr)  
Week 12 Machine learning based systems: fundamentals, tools and practice Online class (4 hr)  
Week 13 1. Deliverable: Working system demos; 2. Exam review Online class (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 and 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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.