Modelling Complex Software Systems
Subject SWEN40004 (2014)
Note: This is an archived Handbook entry from 2014.
Credit Points: | 12.50 |
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Level: | 4 (Undergraduate) |
Dates & Locations: | This subject is not offered in 2014. |
Time Commitment: | Contact Hours: 36 hours, 3 hours per week. Total Time Commitment: 170 hours |
Prerequisites: | Subject Study Period Commencement: Credit Points: AND One of the following:
Subject Study Period Commencement: Credit Points: OR 433-253 Algorithms & Data Structures |
Corequisites: | None |
Recommended Background Knowledge: | None |
Non Allowed Subjects: |
Students cannot enrol in and gain credit for this subject and: 433-441 Systems Modelling and Analysis |
Core Participation Requirements: |
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry. It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability |
Subject Overview: |
AIMS Mathematical modelling is important for understanding and engineering many facets of digital complex systems. The aim of this subject is for students to understand the range and use of mathematical theories and notations in the analysis of discrete systems, how to abstract the key aspects of a problem into a model to handle complexity, and how models can be employed to verify large-scale complex software systems.
INDICATIVE CONTENT Topics covered will be selected from: concurrent systems modelling, analysis and implementation; process algebra; temporal logic and model checking; probability and stochastic modelling; dynamical systems; cellular automata; agent-based modelling; simulation and analysis of complex systems. |
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Learning Outcomes: |
INTENDED LEARNING OUTCOMES (ILO) On completion of this subject the student is expected to:
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Assessment: |
Project work during semester comprising:
Intended Learning Outcomes (ILOs) 1 to 4 are addressed in the examination ILOs 2, 3, ad 4 are addressed in the assignments, and the pair research project Generic skills are addressed by all assessment items
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Prescribed Texts: | None |
Breadth Options: | This subject is not available as a breadth subject. |
Fees Information: | Subject EFTSL, Level, Discipline & Census Date |
Generic Skills: |
On completion of this subject, students should have the following skills.
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Notes: |
LEARNING AND TEACHING METHODS The subject will be delivered through a combination of lectures, hands-on workshops, individual assignments, and a pair-based project in which students use modelling and simulation to study a complex system.
INDICATIVE KEY LEARNING RESOURCES A package of notes will be made available to the students at the start of the course. An addition reference is: Kramer, Jeff, and Jeff Magee: Concurrency: State Models and Java Programs, John Wiley and Sons, 2nd edition (2006).
CAREERS / INDUSTRY LINKS The ability for software engineers and computer scientists to abstract and analyse complex problems is key to their profession. As software systems continue to be deployed in increasingly complex and critical environments, such as transport control, manufacturing, and healthcare, the tools and methods for analysing complex systems will become more important. |
Related Course(s): |
Master of Information Technology Master of Information Technology Master of Information Technology |
Related Majors/Minors/Specialisations: |
B-ENG Software Engineering stream Master of Engineering (Software with Business) Master of Engineering (Software) |
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