Outline

Overview

This unit introduces the foundations of software design and construction. It covers the topics of modelling software (UML, CRC, use cases), software design principles, object-oriented programming theory (inheritance, polymorphism, dynamic subtyping and generics), and simple design patterns. The unit aims to foster a strong technical understanding of the underlying software design and construction theory (delivered in the lecture) but also has a strong emphasis of the practice, where students apply the theory on practical examples.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFO3220 or SOFT2201
Assumed knowledge ?	COMP9103 or COMP9003 (or equivalent UoS at a different institution)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Hong Jin Kang, hongjin.kang@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 Final Exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Early Feedback Task MCQs covering Python and OOP	5%	Week 03 Due date: 22 Aug 2025 at 23:59 Closing date: 29 Aug 2025	N/A	AI allowed
Outcomes assessed:
Out-of-class quiz	Assignment 1: Software Design Principles MCQs about software design and UML diagrams	10%	Week 05 Due date: 05 Sep 2025 at 23:59 Closing date: 12 Sep 2025	n/a	AI allowed
Outcomes assessed:
Creative work	Assignment 2: Refactoring code and applying design patterns Given several programs, identify the design patterns that have been used, point out opportunities for refactoring, and then refactor them.	10%	Week 07 Due date: 19 Sep 2025 at 23:59 Closing date: 26 Sep 2025	n/a	AI allowed
Outcomes assessed:
Creative work	Assignment 3: Software Testing Given a codebase, assess an existing test suite on its level of adequacy. Then, write a new test suite that is more adequate.	10%	Week 09 Due date: 10 Oct 2025 at 23:59 Closing date: 17 Oct 2025	n/a	AI allowed
Outcomes assessed:
Creative work	Assignment 4: Exam Preparation Practice for the exam	10%	Week 12 Due date: 31 Oct 2025 at 23:59 Closing date: 07 Nov 2025	n/a	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly Quizzes Submission on Canvas from week 3 to week 12	5%	Weekly	n/a	AI allowed
Outcomes assessed:
= hurdle task ? = early feedback task ?

Assessment summary

Early feedback task: MCQ covering Python and OOP

Weekly quizzes: There will be weekly quizzes from week 3 to 12 covering the contents of the lectures.

Assignments: There will be four assignments in total.

Final exam: Students will be assessed in an exam taken on campus with supervision. Further information about the delivery of the exams will be provided closer to the test exam.

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.

Minimum Pass Requirement

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the final examination. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

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 10 calendar days late, a mark of 0 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:

Late Policy: For weekly quizzes, late work is not accepted. For assignments submitted late without special consideration or a simple extension, and before the closing date, a penalty of 5% of the available marks is applied for each calendar day late. Late work is not accepted after the closing date, as solutions are released at that time.

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:

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 to software construction and design	Lecture (2 hr)
Week 02	Revisiting OO theory. Introduction to UML Diagrams.	Lecture and tutorial (4 hr)
Week 03	Introduction to Design Patterns. Creational design patterns I	Lecture and tutorial (4 hr)
Week 04	Structural design patterns I. Behavioural design patterns I.	Lecture and tutorial (4 hr)
Week 05	Creational design patterns II	Lecture and tutorial (4 hr)
Week 06	Structural design patterns II	Lecture and tutorial (4 hr)
Week 07	Introduction to Software Testing. Blackbox and Whitebox Testing I.	Lecture and tutorial (4 hr)
Week 08	Blackbox and Whitebox Testing II. Behavioural design pattern I	Lecture and tutorial (4 hr)
Week 09	Behavioral design patterns II	Lecture and tutorial (4 hr)
Week 10	Behavioral design patterns III. Recap of software design patterns.	Lecture and tutorial (4 hr)
Week 11	Designing interfaces and their specifications. Debugging.	Lecture and tutorial (4 hr)
Week 12	Code Reviews	Lecture and tutorial (4 hr)
Week 13	Unit review and exam preparation	Lecture and tutorial (4 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.

Required readings

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

Eric Freeman, Elisabeth Robson, Head First Design Patterns, O'Reilly Media, Inc, USA; 2 edition (5 January 2021)
Craig Larman, Applying UML and Patterns (3rd). Pearson.
Gamma, Helm, Johnson, Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software. Addison Wesley, 2004. 9780201633610.

Optional -- not reserved:

Software Testing - A Craftsman's Approach (4th Edition) Paul C Jorgensen
A Philosophy of Software Design John Ousterhout

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. explain processes and elements in object-oriented design
LO2. apply object-oriented design approaches to software design
LO3. apply object-oriented design principles to implementation using an OO programming language
LO4. object oriented theory including inheritance, polymorphism, dynamic binding, subtyping and generics
LO5. discuss and apply basic testing techniques and code review to software systems

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 assignments' content and the due dates to spread the work.

Additional information

Knowledge of OO theory and Java programming is required

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.Inassessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

COMP9201: Software Construction and Design 1

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links