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Regular version of the site

Research Seminar "Software Engineering: Development Management"-1

2022/2023
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Compulsory course
When:
1 year, 1, 2 module

Instructor

Course Syllabus

Abstract

Modules 1 and 2 of the research seminar "Software Engineering: Development Management" are aimed at studying the features of software development automation practices and culture. While mastering the course program, students will gain understanding of the basic industrial development tools and basic devops practices. In addition to theoretical knowledge, students will receive practical tasks that will help to form the skills of applying and implementing a modern technology stack. During the course, we will get acquainted with the culture of DevOps, rehearsing each stage of software development and automation by mastering the practices and used tools. Modules 3 and 4 are focused on getting to know the area of process mining through the lens of discussing research papers. Modern information systems accumulate significant amounts of event data, which include, for instance, transaction logs, message logs and different records of user activity. These data are commonly referred to as event logs consisting of ordered event sequences called traces. Event logs are used in process mining to discover models of real processes. The expected behavior of an information system is usually specified at the beginning stages of the life cycle. Discovering the real behavior of processes from event logs is an important problem, since manually created models do not reflect changes made during the operation period of an information system. A wide range of algorithms for the automated discovery of process models have been proposed over recent years. The behavior of processes can be represented in various notations, including different classes of Petri nets, heuristic and causal nets, or Business Process Model and Notation (BPMN). The quality of process discovery algorithms is determined by the quality of discovered process models. Conformance checking is another important part of process mining. Conformance checking offers quality dimensions, which measure the correspondents between an event log (observed behavior) and a process model as well as the complexity of discovered models. Data acquired during conformance checking form the basis for the further enhancement and the reliability improvement of processes, occurring in information systems. Students will familiarize themselves with the fundamental principles and key application areas of process discovery, conformance checking and process enhancement by presenting and reviewing reports based on conference and journal papers.