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Unit outline_

ELEC1005: Introduction to Software Engineering

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit of study will introduce student to the field of software engineering. It will expose the students to fundamental (basic) concepts of key areas within software engineering including Requirement Engineering, Software Design (Architecture and Modeling), Software Quality Engineering, and Software Process Engineering. This unit also provides students with practical experience to start to develop the required engineering skills through case studies of authentic open-source software projects and hands-on development experience of software projects in a group, as well as communication, documentation, and presentation skills. The unit will provide students with a sound foundation for the further study of software engineering.

Unit details and rules

Academic unit School of Electrical and Computer Engineering
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
ENGG1800 or CHNG1108 or MECH1560 or AERO1560 or BMET1960 or MTRX1701 or ELEC1004
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Dong Yuan, dong.yuan@sydney.edu.au
Tutor(s) Charles Liu, zhenzhong.liu@sydney.edu.au
Fan Huang, fan.huang@sydney.edu.au
Laicheng Zhong, laicheng.zhong@sydney.edu.au
Yuning Zhang, yuning.zhang1@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final Exam
closed book test
45% Formal exam period 2 hours
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Small test Quiz
online test
10% Week 08 1 hour
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project one
Group project, submit on Canvas
20% Week 09 The first half of the semester
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project two
Group project, submit on Canvas.
25% Week 13 The second half of the semester.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
group assignment = group assignment ?

Assessment summary

All the tests submissions are based on Canvas. 

Assessment criteria

Fail [0, 50)
Pass [50, 65)
Credit [65, 75)
Distinction [75, 85)
High Distinction [85, 100]

 

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:

20% per day

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 Introduction & Planning Lecture (2 hr) LO1 LO6
Week 02 Planning for Software Engineering. Lab: JIRA, Confluence, Project Lecture and tutorial (4 hr) LO1 LO3
Week 03 Analysis. Lab: Planner, ToDo, Whiteboard Lecture and tutorial (4 hr) LO1 LO3 LO4
Week 04 Designing Lab: Figma, Viso, Forms Lecture and tutorial (4 hr) LO1 LO3 LO4
Week 05 Development. Lab: Bitbucket, Sharepoint Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 06 Implementation. Lab: Power Apps Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 07 Testing & Debugging. Lab: SharPoint Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 08 Verification & Validation. Lab: Project 1 Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 09 Integration & Deployment. Lab: Project 1 (10 mins Presentation) Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 10 Maintenance & Documentation. Lab: PowerAutomation Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 11 Advanced Tech. Lab: ChatBot Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Iteration. Lab: AI Builder Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Wrap up and revision. Lab: Project 2 (10 mins Presentation) Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6

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 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. Obtain a broad knowledge of the sub-disciplines within software engineering, and demonstrate applications of basic engineering concepts in the software engineering discipline.
  • LO2. Work in a team in a software engineering project, and enhance oral communication skills by presenting in front of a group.
  • LO3. Understand some of the fundamentals of different types of programming languages and software development tools and technologies.
  • LO4. Apply some introductory analysis techniques to understand user requirements and problem-solving methods to design simple software functions.
  • LO5. Develop basic skills in the use of software development processes, and understand how a range of software processes are used to develop software components and to have hands-on experience with some of them.
  • LO6. Develop a high-level understanding of the course content and curriculum within the software engineering degree, and understand the role of a graduate software engineer.

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.

We have redesigned the labs based on last year's feedback. The lecture contents have been updated too.

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.