COMP9110: Semester 2, 2020

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Units COMP9110 Semester 2 2020 [Normal day]

Unit outline_

COMP9110: System Analysis and Modelling

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit provides a comprehensive introduction to the analysis of complex systems. Key topics are the determination and expression of system requirements (both functional and non-functional), and the representation of structural and behavioural models of the system in UML notations. Students will be expected to evaluate requirements documents and models as well as producing them. This unit covers essential topics from the ACM/IEEE SE2004 curriculum, especially from MAA Software Modelling and Analysis. Note: The lectures of this unit are co-taught with ISYS2110.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ELEC3610 OR ELEC5743 OR INFO2110 OR INFO5001 OR ISYS2110
Assumed knowledge ?	Experience with a data model as in COMP9129 or COMP9103 or COMP9220 or COMP9120 or COMP5212 or COMP5214 or COMP5028 or COMP5138
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Vera Chung, vera.chung@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final Exam 2 hours written examination	70%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1: System requirements Report	12%	Week 09	N/A
Outcomes assessed:
Assignment	Assignment 2: System model Report and presentation	18%	Week 12	N/A
Outcomes assessed:
= Type C final exam ?

Assessment summary

Assignment: Assignment 1: System Requirements (in teams). Elicit and document system requirements; deliver/explain these in oral presentation.
Assignment: Assignment 2: System Modelling. Following on from the previous assignment the teams will assemble descriptions of the system using appropriate notations.
Final Exam: Written Examination. Covering all aspects of the unit.

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.

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	1. Course introduction; 2. Overview of SDLC	Lecture and tutorial (3 hr)
Week 02	Requirements (Functional)	Lecture and tutorial (3 hr)
Week 03	Requirements (Non-functional)	Lecture and tutorial (3 hr)
Week 04	Project planning and management	Lecture and tutorial (3 hr)
Week 05	Human computer interaction layer design	Lecture and tutorial (3 hr)
Week 06	Structural models of the domain (1): Introduction to class and object diagrams	Lecture and tutorial (3 hr)
Week 07	1. Structural Models of the domain(2): Class and object diagrams; 2. Using CRC cards in analysis	Lecture and tutorial (3 hr)
Week 08	1. Introduction to HTML; 2. Modelling of rule based system	Lecture and tutorial (3 hr)
Week 09	Physical and system architecture design	Lecture and tutorial (3 hr)
Week 10	Behavioural models (interaction diagram)	Lecture and tutorial (3 hr)
Week 11	1. Behavioral models in analysis; 2. Interaction diagrams; 3. State diagrams	Lecture and tutorial (3 hr)
Week 12	Construction (testing & documentation) and course review	Lecture and tutorial (3 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.

Dennis, A., Wixom, B.H., and Tegarden, D – System Analysis & Design (An Object oriented approach with UML): John Wiley & Sons, 2015

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. Identifying alternative system solutions and assess their feasibility
LO2. Able to work with requirements documents, to identify aspects of requirements including functional, performance and usability conditions
LO3. Able to work with data modelling based on a substantial realistic context; An awareness of the tasks involved when working with conceptual data model documents, along with the ability to create, interpret and evaluate UML class structure diagrams
LO4. Able to work with the process modelling based on a substantial realistic context; An awareness of the tasks involved when working with process model documents, along with the ability to create, interpret and evaluate UML message sequence diagrams, collaboration diagrams, activity diagrams and state-chart diagrams
LO5. Able to relate different diagrams (e.g. to identify inconsistencies between them)
LO6. Able to produce clear well-constructed technical documents and diagrams. Able to produce and deliver an oral presentation.
LO7. Able to understand of the stages in the process of developing an information system, and the relationship to the organisational context (especially the role of systems analysts interacting with other stakeholders); able to understand of the way the process uses documents such as requirements descriptions and analysis models.
LO8. Able to aware of risk issues, and of methods of dealing with them, including cost-benefit analyses, project planning and management. Able to work with project planning documents including Gantt charts and detailed Work Breakdown Structures

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.

No significant changes have been made since this unit was last offered. This unit has changed to deliver in 12 weeks.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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