COMP2017: Semester 1, 2025

Overview

In this unit of study, elementary methods for developing robust, efficient, and re-usable software will be covered. The unit is taught in C, in a Unix environment. Specific coding topics include memory management, the pragmatic aspects of implementing data structures such as lists and hash tables and managing concurrent threads. Debugging tools and techniques are discussed and common programming errors are considered along with defensive programming techniques to avoid such errors. Emphasis is placed on using common Unix tools to manage aspects of the software construction process, such as version control and regression testing. The subject is taught from a practical viewpoint and it includes a considerable amount of programming practice.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	INFO1113 or INFO1105 or INFO1905 or INFO1103
Corequisites ?	COMP2123 or COMP2823 or INFO1105 or INFO1905
Prohibitions ?	COMP2129 or COMP9017 or COMP9129
Assumed knowledge ?	Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra (e.g. MATH1061 or equivalent)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	John Stavrakakis, john.stavrakakis@sydney.edu.au
Demonstrator(s)	Ryan Padamadan, ryan.padamadan@sydney.edu.au
	Daniel Geng, ruize.geng@sydney.edu.au
	Yao Ke, yao.ke@sydney.edu.au
Lecturer(s)	John Stavrakakis, john.stavrakakis@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Pen and paper examination	50%	Formal exam period	2 hours
Outcomes assessed:
Small test	T0 Complete a test for tracing and writing code. #earlyfeedbacktask	10%	Week 02 Due date: 08 Mar 2025 at 09:00 Closing date: 15 Mar 2025	50 minutes
Outcomes assessed:
Assignment	P1 Write, debug, and address questions about a programming problem	10%	Week 06 Due date: 02 Apr 2025 at 23:59 Closing date: 20 Apr 2025	21 days
Outcomes assessed:
Assignment	P2 Write, debug, and address questions about a programming problem	10%	Week 12 Due date: 20 May 2025 at 23:59 Closing date: 03 Jun 2025	21 days
Outcomes assessed:
Small continuous assessment	In class tasks Activity to complete during tutorial class	20%	Weekly	5 - 20 minutes
Outcomes assessed:
= hurdle task ? = AI allowed ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Small test

Early feedback task. Complete a test which involves to tracing and writing code. Refer to your timetable for the time and location of the examination. Further information will be available from your unit coordinator.

In class tasks

Complete activity during the tutorial class. Tutor may direct students to complete a paper quiz, computer programming problem, online quiz or a code interview. Refer to Lecture 1 for the schedule. Only 8 of the available tasks will be formally graded, while others are used for feedback.

P1, P2

Students must independently solve programming problems by writing, debugging, and submitting solutions based on a provided problem description. Additionally, they must create and submit test cases to evaluate their code. All work must be completed and submitted by the due date.

Final examination

The final exam counts for up to 50% of the course grade. On the examination day, students must choose one question booklet to attempt; only that booklet will be graded. The exam's impact on the final grade will vary based on the grading criteria.

The examination will cover the course contents. Students will be asked to demonstrate knowledge in systems programming concepts, reading and tracing through memory related issues. Write short programs. Write test cases and debug with existing test cases. Information about the examination format and materials required will be available in Week 13 Lecture.

Examination barrier depends on the question booklet chosen. Details about each are presented in the course. For non-standard examinations, further submitted work may be required if the student had not demonstrated required competencies in certain learning outcomes.

Conditions for pass in this course

Satisfy final examination barrier
At least 50% total

Special consideration

Approved special consideration may granted additional time to complete the assignment, or require a viva voce. The viva voce can be based on the assignment contents and that contributes to the grade of the assignment.

Further information about assessments

Lecture 1 will include information about assessment conditions and submission instructions

Detailed information for each assessment can be found on the course website upon release: edstem.org

Use of language translation tools for all online assessments is forbidden. All answers must be provided in the English language, including code comments.

Students may be asked for further development of their assessments if they fail to attend at least 80% of their tutorials or have approved special consideration.

It is a requirement of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. 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.

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.

Automatically graded submissions provide feedback. The tutors will provide further feedback after the deadline about correctness, style and testing.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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 2023 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 2023. 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	Course introduction and Administration	Lecture (1 hr)
Week 01	Intro to UNIX/C	Tutorial (1 hr)
Week 01	Introduction to C	Lecture (2 hr)
Week 02	C programs with text processing	Tutorial (2 hr)
Week 02	Addressable memory 1, string and arrays	Lecture (2 hr)
Week 03	C pointers and C library functions	Tutorial (2 hr)
Week 03	Addressable memory 2, structures and files	Lecture (2 hr)
Week 04	Structs, Unions, Bitfields and Files	Tutorial (2 hr)
Week 04	Memory management and linked lists	Lecture (2 hr)
Week 05	Dynamic memory and debugging	Tutorial (2 hr)
Week 05	Function pointers, Signals	Lecture (2 hr)
Week 06	File IO, Function pointers and Signals	Tutorial (2 hr)
Week 06	Preprocessor and Linking	Lecture (2 hr)
Week 07	Compiler pipeline, Signals, Makefile and Shared Library	Tutorial (2 hr)
Week 07	Parallelism and concurrency. Processes and fork	Lecture (2 hr)
Week 08	Processes and fork	Tutorial (2 hr)
Week 08	Interprocess communication IPC	Lecture (2 hr)
Week 09	IPC. Shared memory and Pipes	Tutorial (2 hr)
Week 09	Thread safety and synchronisation 1	Lecture (2 hr)
Week 10	Parallelism with POSIX threads and optimisations	Tutorial (2 hr)
Week 10	Thread safety and synchronisation 2	Lecture (2 hr)
Week 11	Synchronisation and Atomics	Tutorial (2 hr)
Week 11	Scalable algorithm templates	Lecture (2 hr)
Week 12	Memory usage patterns and overflow	Tutorial (2 hr)
Week 12	Performance of parallel programs	Lecture (2 hr)
Week 13	Review	Tutorial (1 hr)
Week 13	Revision and examination overview	Lecture (2 hr)

Attendance and class requirements

IMPORTANT:

School guidelines relating to Academic Dishonesty and Plagiarism. In assessing 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.

Course websites:

The course website on edstem.org will contain information, including important announcements. Teaching staff will be communicating to all students and it is considered part of the course. Students are expected to regularly visit this website to know these announcements and information concerning format and schedule of assessment. Canvas is a website that will be used to disseminate the online lecture recordings and for publishing of results.

Attendance:

Students are asked to attend their tutorial class each week as part of their assessment. Students are advised to follow the procedures concerning late attendance, or failure to attend. Such procedures will be presented in the course lectures.

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 on the Library eReserve link available on Canvas.

Computer Systems: A Programmer’s Perspective, Randal E. Bryant and David R. O`Hallaron, 9781292101767, 3rd edition, Pearson Education, 2016, Boston

Reference books

Brian W. Kernighan and Dennis M. Ritchie – The C Programming Language. Prentice Hall. 1988. 0-13-110362-8
Lin and Snyder. Principles of Parallel Programming. Pearson Education. 2008
Jeri R. Hanly, Elliot B. Koffman. Problem Solving and Program Design in C. 6th Edition. Addison Wesley. 2010. ISBN:0321198034. Note: 4th edition does not contain the chapter on IPC
Paul Davies. The Indispensable Guide to C. 1st Edition. 1995. ISBN-13: 978-0201624380

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. apply code quality strategies appropriate for C, including preprocessor techniques, and use of common idioms
LO2. use Unix commands and system calls (including usage of flags etc) from online manual system
LO3. demonstrate the approach and concepts of Unix, including its tools philosophy, processes (including pipes and redirection), the file system, and the shell
LO4. compose correct, clean code in C that allocates, deallocates and manages memory
LO5. construct correctly implement standard linked list data structures. Higher performance could involve slightly more complicated structures such as binary search trees
LO6. assess code execution using debugging tools
LO7. apply a thorough automated testing regime using tools such as make, diff, scripts to present the outcomes, and a tool to manage regression testing. Higher performance could involve ability to construct such a regime
LO8. read and write code that correctly uses the main standard library functions, especially for I/O, file handling, and string handling. Higher performance could involve elegant use of these functions, particularly avoiding idioms which are extremely inefficient.
LO9. evaluate common memory-related errors (such as memory leaks, dangling pointers) and how to avoid these. Higher performance could involve detecting errors in example code, and fixing them using debuggers
LO10. construct, debug, and evaluate parallel or concurrent programs.
LO11. Understand and identify security vulnerabilities in memory usage patterns.

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.

Change of assessment loading

Additional information

Every week students must:

Read the required sections of literature
Attend and take notes for the Lecture
Make progress on and complete the assessments (as required)
Prepare for the Lab by reviewing reading, lecture and lab questions
Attend and participate in weekly Lab with tutor(as timetabled)

Additionally:

Students should ask questions on edstem.org

Access to online assessments. Access to course contents: Lecture, Laboratory, online lessons, challenges etc. Access to important announcements, discussion and support forum.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 03 Mar 2025.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

COMP2017: Systems Programming

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Small test

In class tasks

P1, P2

Final examination

Conditions for pass in this course

Special consideration

Further information about assessments

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

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