Modelling and Analysis of IT Systems 510-IS2-1MASI-23
Profile: general academic
Form of studies: full-time
Course type: obligatory
Field and discipline of science: exact and natural sciences, computer science
Year of studies/semester: 1 / 1
Preliminary requirements (so-called sequential system of classes and examinations): ---
Number of class hours:
- lecture: 15 h
- laboratory classes: 15
Didactic methods: lecture, presentation, discussion, laboratory classes, project.
ECTS credits: 3
Details of student's workload (activity and number of hours):
- Attendance at classes:
- - lecture : 15 h
- - laboratory: 15 h
- Preparation for classes:
- - lecture: 4 h
- - laboratory: 6 h
- Literature studying: 6 h
- Student's own works on computer programs, projects, reports, presentations, homeworks: 9 h
- Preparation for exam: 8 h
- Attendance at exam: 2 h
- Attendance at office hours: 10 h
Quantitative indicators:
- Student's workload related to activities that require direct participation of a teacher: 42 h / 1,7 ECTS
- Student's workload related to activities that do not require direct participation of a teacher: 33 h / 1,3 ECTS
Type of course
Mode
(in Polish) w sali
Course coordinators
Learning outcomes
- Has extended knowledge of analyzing and object-oriented modeling of software systems and databases. KP7_WG3, KP7_WG5
- Knows essential design issues related to system architecture, server infrastructure, and QoS. KP7_WG3, KP7_WG5
- Is able to create, analyze, and optimize an object model of a moderately complicated IT system. KP7_UW1
- Is able to retrieve, evaluate, and integrate informations necessary to model a system. KP7_UU1
- Is able to analyze a system implementation with respect to model conformance and requirement satisfaction. KP7_UU1, KP7_UW1
- Is able to create a detailed documentation of an IT system/project. KP7_UK4, KP7_UO3
- Knows how to work in group. KP7_UO2, KP7_UO3
- Creatively solves problems, learning when necessary. KP7_UO4, KP7_UU2
Assessment criteria
Credit type: examination
Students cannot be examined before passing the laboratory.
Rules of AI usage v.25.09.29
Students can use AI systems only to the extent permitted by law acts adopted by the University and Faculty, to automatize mechanic actions that require nether creative nor critical thinking, and that do not require understanding processes and technologies.
Students are prohibited from using AI to do tasks that aim to develop students' creativity, skills and knowledge, that should be solved without automatization in order to ensure the learning outcomes.
Questions and doubts about AI usage should be communicated to the teacher.
Bibliography
Essential bibliography:
- B. Burns, Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services, O'Reilly, 2018
- M. Kleppmann, "Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", O'Reilly, 2017
- K.P. Birman, "Guide to Reliable Distributed Systems: Building High-Assurance Applications and Cloud-Hosted Services", Springer, 2012
Supplementary readings:
- T. Szigeti, C. Hattingh, R. Barton, K. Briley, "End-to-End QoS Network Design: Quality of Service for Rich-Media & Cloud Networks", 2nd ed., Cisco Press, 2013.
- E. Gamma, R. Helm, R. Johnson, J. Vlissides, Wzorce projektowe. Elemeny oprogramowania obiektowego wielokrotnego użytku, Helion, 2010
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: