Course Details for A.Y. 2018/2019
Name:
Software Engineering For Autonomous Systems / Software Engineering for Autonomous Systems
Basic information
Credits:
: Master Degree in Computer Science 6 CFU (b)
Degree(s):
Master Degree in Computer Science 1st anno curriculum GSEEM Elective
Master Degree in Computer Science 2nd anno curriculum NEDAS Elective
Master Degree in Computer Science 1st anno curriculum SEAS Elective
Master Degree in Computer Science curriculum UBIDIS Elective
Language:
English
Course Objectives
Systems that can change their behaviour in response to unanticipated events during operation are called “autonomous”. Unlike more traditional systems that
have predefined purposes, autonomous systems are able to tailor their behaviour and operations in accordance with the circumstances they find. Engineering autonomous systems is a challenging task involving several theoretical foundations and application fields (e.g., self-adaptiveness, machine learning, sensor networks, control engineering, and artificial intelligence).
This course aims at introducing the fundamental concepts related to the development of autonomous systems from a software engineering perspective. Various methods and techniques currently applied in the design of autonomous systems are shown. Self-* attributes of autonomous systems, architectures, models, and languages are presented in order to show the technical viability of systems that can dynamically adapt their behaviour to varying operating conditions, delivering the appropriate application level response under these different conditions. Concrete examples of autonomous systems in the domains of Internet of Things, Cyber-Physical Systems, and unmanned vehicles are given.
Course Content
- Autonomous systems
- Self-adaptive systems
- Software Engineering
- Model-Driven Engineering
- Models@runtime
- Internet of Things
- Cyber-Physical Systems
- Unmanned Vehicles
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should
- On successful completion of this module, the student should:
- Be able to use modeling languages to model autonomous systems.
- Be able to adopt model-driven techniques and tools to engineer autonomous systems.
- Be able to apply knowledge of computing and mathematics appropriate to the discipline.
- Be able to analyze a problem, identify and define the computing requirements appropriate to its solution.
- Be able to function effectively on teams to accomplish a common goal.
- Be able to communicate effectively with a range of audiences.
- Be able to analyze the impact of computing on individuals, organizations and society, including ethical, legal, security and global policy issues.
- Be able to use current techniques, skills, and tools necessary for computing practice.
- Have developed practical skills that can be transferred into a real-world environment.
Assessment Methods and Criteria
- Pre-Assessment: There is no formal pre-assessment, but Course pre-requisites are clearly stated on the Module website. Fulfilment of such pre-requisites is verified by formative assessment.
- Formative Assessment: The formative assessment is performed via interactive interaction between teacher and students during lectures. Students are aware since the beginning of the Course that they will be involved (in turns) in: - Questioning and discussion, by means of open oral questions to the class or to single students.
- Summative Assessment: Group project followed by an optional oral exam.
The group project is aimed at: (1) verification of theoretical competences, and in particular of knowledge and comprehension of Course contents; (2) verification of skills in understanding and solving significant problems, and in explaining the proposed solutions; (3) capability of collaborative work. This in order to verify the ability of application of techniques learnt during the Course, of analysis of problems and synthesis of suitable solutions, and of evaluation of alternative solutions. Criteria of evaluation will be: the level of knowledge and practical ability; the property of use of the technical language; the clarity and completeness of explanations. The oral exam will occur within one week of the project delivery and will typically cover the areas of the project that need clarification plus additional subjects proposed by the teacher.
- Assessment breakdown: 100% end-of-semester summative assessment.
Course page updates
This course page is available (with possible updates) also for the following academic years:
To read the current information on this course, if it is still available, go to the university course catalogue .
Course information last updated on: 22 novembre 2016, 15:55