Outline

Overview

Model-Based Software Engineering focuses on modern software engineering methods, technologies, and processes used in professional development projects. It covers both the pragmatic engineering elements and the underlying theory of the model-based approach to the analysis, design, implementation, and maintenance of complex software-intensive systems. Students will participate in a group project, which will entail developing and/or evolving a software system, following a full development cycle from requirements specification through to implementation and testing using up-to-date industrial development tools and processes. At the end of the course they will provide a presentation and demonstration of their project work to the class. There is no formal teaching of a programming language in this unit, although students will be expected to demonstrate through their project work their general software engineering and architectural skills as well as their mastery of model-based methods and technologies. Students successfully completing this unit will have a strong practical and theoretical understanding of the modern software development cycle as applied in industrial settings. In particular, they will be familiar with the latest model-based software engineering approaches necessary for successfully dealing with today's highly complex and challenging software systems. The pedagogic grounds for this course and its focus on model-based approaches are to arm new software engineers with skills and perspectives that extend beyond the level of basic programming. Such skills are essential to success in software development nowadays, and are in great demand but very low supply. The dearth of such expertise is one of the key reasons behind the alarmingly high failure rate of industrial software projects (currently estimated at being greater than 40%). Therefore, this unit complements SQE and strengthens a key area in the program.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	A programming language, basic maths.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Dong Yuan, dong.yuan@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam n/a	30%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Project stage 1 project modelling and design	35%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Project stage 2 project implementation	25%	Multiple weeks	n/a
Outcomes assessed:
Online task	Mid-term exam n/a	10%	Week 09	n/a
Outcomes assessed:
= group assignment ? = Type D final exam ?

Assessment summary

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.

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:

Generally 20% penalty marks per day. Special cases will be considered.

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	Introduction to software architecture	Online class (2 hr)
Week 02	Introduction Model based software engineering	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Group formation, Introduction to the project, Project stage 1 start	Computer laboratory (2 hr)
Week 03	Requirement Modelling	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Laboratory: Project stage 1 (modelling requirement)	Computer laboratory (2 hr)
Week 04	Architecture Design and Modeling	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project stage 1 (architecture modelling)	Computer laboratory (2 hr)
Week 05	Modeling Structure	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project stage 1 (structure modelling)	Computer laboratory (2 hr)
Week 06	Modeling Behaviour	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project stage 1 (behaviour modelling 1)	Computer laboratory (2 hr)
Week 07	Metamodeling and Software Modeling Languages	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project stage 1 (behaviour modelling 2)	Computer laboratory (2 hr)
Week 08	Public Holiday	Independent study (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project stage 1 Q&A	Computer laboratory (2 hr)
Week 09	Agile software development model, midterm test	Online class (2 hr)
	Q&A of lecture	Tutorial (1 hr)
	Project Stage 1 due, Project stage 2 start	Computer laboratory (2 hr)
Week 10	Modern Computing Platforms for Software Modelling	Online class (2 hr)
	group presentations for project stage 1	Tutorial (1 hr)
	group presentations for project stage 1	Computer laboratory (2 hr)
Week 11	Software Development Process Models	Online class (2 hr)
	Tutorial Q&A of lecture	Tutorial (1 hr)
	Project stage 2 implementation	Computer laboratory (2 hr)
Week 12	Advanced Topics	Online class (2 hr)
	Tutorial Q&A of lecture	Tutorial (1 hr)
	Project stage 2 implementation	Computer laboratory (2 hr)
Week 13	Unit Review	Online class (2 hr)
	Project demonstration	Tutorial (1 hr)
	Project demonstration	Computer laboratory (2 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.

Thomas Stahl, Markus Voelter, and Krzysztof Czarnecki, Model-Driven Software Development: Technology, Engineering, Management (first). Wiley, 2006. 13: 978-0470025703.

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. understand approaches to MBSE technology and methodology: automated code generation, model analysis methods, tooling, architectural design, system and multi-model development
LO2. understand MBSE process models
LO3. demonstrate in-depth knowledge of the UML 2 modeling language
LO4. understand the role and nature model-based methods in SE
LO5. understand the theory of modeling language design
LO6. study MBSE systems based on comprehensive research in the open literature
LO7. write professional reports and do class presentations on a system design and its performance
LO8. work smoothly as a member of a project team.

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.

The lecture contents will be improved according to students' feedbacks.

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

On this page

Useful links

Media

Student links

About us

Connect

Current students

ELEC5620: Model Based Software Engineering

Semester 2, 2021 [Normal day] - Remote

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

On this page

Useful links

Media

Student links

About us

Connect