Outline

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 ?	None
Corequisites ?	None
Prohibitions ?	COMP2129 OR COMP2017 OR COMP9129
Assumed knowledge ?	COMP9003
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	John Stavrakakis, john.stavrakakis@sydney.edu.au
Tutor(s)	Andrew Xu, andrew.xu@sydney.edu.au
Tutor(s)	Sam Arch, sarc9328@uni.sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final examination Computer examination	30%	Formal exam period	3 hours
Outcomes assessed:
Assignment	A1 Solve a programming problem	10%	Week 04 Due date: 17 Mar 2022 at 23:59	17 days
Outcomes assessed:
Assignment	A2 Solve and explain programming problem to instructor	30%	Week 06 Due date: 31 Mar 2022 at 23:59	30 days
Outcomes assessed:
Assignment	A3 Solve and explain programming problem to instructor	30%	Week 11 Due date: 12 May 2022 at 23:59	30 days
Outcomes assessed:
= Type D final exam ?

Assessment summary

A1

Solve and explain programming problem. Students will be provided with a description of the problem, will write a solution that is to be completed independently, submit by the due date, and be evaluated by input/output tests.

A2, A3

Solve and explain programming problem to instructor. Students will be provided with a description of the problem, will write a solution that is to be completed independently, submit by the due date, and be evaluated by explaining their solution and answer questions from the instructor.

Students will need to be available to interact with the instructor online through video conferencing software, with screen sharing as a viva voce style examination.

Marks are awarded only when both a solution is submitted, and a viva voce is performed. Automated testing of the program will contribute to the grade.

Final examination

The final exam can cover any aspect of 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.

Conditions for pass in this course

At least 40% in the assessment grade
At least 50% in final examination
At least 50% total

In order to pass this unit, a student must achieve at least 40% in the formative assessment and 50% in the final exam. 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.

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: edstem.org

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.

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.

The tutors will provide further feedback to students about correctness, style and testing.

Automatically graded submissions provide further feedback.

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:

No late submissions are accepted for any assessments.

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	Admin/introduction to C	Lecture (2 hr)
Week 02	First C programs with text processing	Tutorial (2 hr)
Week 02	Addressable memory 1, strings 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 buffer overflow	Tutorial (2 hr)
Week 12	Performance of parallel programs	Lecture (2 hr)
Week 13	Revision	Tutorial (2 hr)
Week 13	Revision and examination overview	Lecture (2 hr)

Attendance and class requirements

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.

Online 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.

Fewer assessments and better scheduling

Additional information

Every week students must:

Read the required sections of literature
Attend and take notes for the Lecture (Mondays)
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.

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

Current students

COMP9017: Systems Programming

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

A1

A2, A3

Final examination

Conditions for pass in this course

Further information about assessments

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

Additional information

Additional information

Disclaimer

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Media

Student links

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